2. Chapter 1 “Analysis of existing approaches to conducting computer command and staff war games.”
3. Chapter 2 “Formalization of computer command and staff war games.”
4. Chapter 3 “Methodology for designing an information process control manager when conducting computer command and staff war games.”
5. Chapter 4 “Experimental studies of the effectiveness of information process management during computer command and staff war games.”
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Introduction of the dissertation (part of the abstract) on the topic “Simulation modeling when conducting computer command and staff war games”
The results of the analysis of military conflicts, as well as the main provisions of military doctrines and the views of military specialists from NATO countries on the combat use of air attack weapons (AEA) determine the increasing requirements for officials of military air defense command and control agencies to ensure reliable cover of troops and facilities. One of the effective approaches to an unconventional solution to the problems of operational and combat training of command personnel in the current conditions is the use of computer technology and achievements in the field of simulation and mathematical modeling of control systems and processes. An analysis of the ongoing research has shown that the considered approaches to the implementation of computer-based forms of operational training (CFOP), a type of which are command and staff war games (CSWG), from a technical point of view, provide for the widespread use of computer networks based on personal computers.
When implementing CFOP, in comparison with existing automated control systems for troops, the types of information exchange channels change and their number is reduced; in fact, the information topology of real automated control systems is transformed into a local computer network. In addition, there is a need to model information of various types over one information channel, for which separate independent channels are allocated in real automated control systems. At the same time, it is necessary to ensure compliance of the tasks solved during computer-based control systems (CSVIs) with the logic of the operation of real controls, as well as the efficiency and functional completeness of their implementation. In addition, the specifics of conducting CCSHVI determine the need to solve a number of additional tasks related to the implementation of the functions of playing along and monitoring the actions of game participants. These features of information exchange during computer-based data transmission lead to an increase in the load on the local network and the intensity of data flows circulating in it. In this regard, there is a need to manage these data flows, taking into account the logic, functional orientation and priority of tasks solved during the game, as well as the dependence of the value of the processed information on the delay time for its processing. When implementing computer KSHVI using a system of simulation models, the types of information exchange channels change and their number is reduced.
A comparative analysis of the capabilities of existing dispatch tools for managing information exchange in relation to the tasks solved during computer control monitoring shows that they do not provide a high-quality solution to these problems. Therefore, there is a need to develop specialized tools for managing information processes occurring during computer-based computer monitoring. As such a tool, it is proposed to use an information process control manager (IDIP), which in this work is understood as a software tool that determines the order of processes in a computer network in accordance with accepted agreements and restrictions on the functional, logical and time aspects of their implementation.
The existing methodological apparatus for developing dispatch tools ensures the creation of specialized means for managing information exchange in computer networks, but does not allow its use for the development of DUIP. In this regard, a contradiction arises between the need to develop information process management tools that ensure the technical implementation of the CCIS, and the technological capabilities of the existing methodological apparatus for creating such tools.
Taking into account these circumstances, as well as the prospect of a possible expansion of the list of tasks solved during computer-assisted computer monitoring, it seems relevant to solve the problem of developing a comprehensive methodological apparatus for designing an information process management manager, ensuring an increase in the efficiency of their management, taking into account the specifics of the tasks solved during computer-based computer-based monitoring.
Object of research. The role of the research object in the dissertation work is assigned to the development of air defense functions in the processes of command post exercises (CSE) conducted in a human-computer environment.
Basic settings and ideas. The choice of the subject of research and direction of work was influenced by the following guidelines: U1. Command and staff exercises allow their interpretation in the form of a specific class of war games, which opens up access to theoretical and practical experience of games, including the experience of developing entertaining war games.
U2. Any version of the implementation of hardware-software support for CSG should be built in the form of a client-server application for a local computer network.
Subject of research. The subject of the study is a specialized hardware and software shell that supports the processes of the KShVI, in which the functions of controlling and assessing the progress of the game are focused only on the protective functions of air defense and are closed from the influence of the participants of the KShVI.
Direction of research. The direction of research in the work is the use of a specialized software product in the KShVI in the context of a simulation model of the protective functions of air defense at the “game step”.
Goals and objectives of research. The main scientific goal of the work is related to the search for a theoretical generalization of the implementation of protective functions of air defense in the process of CSVI, managing the conditions for their use, assessing their effectiveness and achieving the required training effects.
The main practical goal is related to the development of an effective dispatch system in a client-server environment that serves the conduct of CSVI. Achieving the stated goals requires solving the following main tasks: 1. To develop and study a simulation model of the command and control system, revealing the preparation, execution and evaluation of the protective functions of air defense in the context of the game interpretation of the command and control system.
2. Develop and research a communication system that takes into account the structure of the composite subject of the exercise and the role functions of each of the participants in the exercise.
3. Based on the specifications of the CCS simulation model, develop a dispatch system that provides control of information flows and their processing at the operational-tactical level.
Research method. The essence of the research method is defined as a controlled combinatorics of methods and means of simulation modeling, theory and practice of games, artificial intelligence and algorithmization. Scientific novelty1. A simulation model of the command and control system with a game interpretation of the actions of the exercise participants has been proposed and studied, providing an integrated representation of the protective functions of the air defense and the specifications of the hardware and software complex serving the conduct of the exercises.
2. A system of structural functional and information specifications for the client-server implementation of KSHVI has been developed and studied, taking into account the dynamics of processes, including communicative ones, in real time.
Credibility. The theoretical reliability of the results obtained is confirmed by the formulation of the main provisions of the dissertation based on reliable knowledge from the field of applied computer science, simulation modeling and game theory.
Experimental confirmation of reliability was obtained during the development of a client-server implementation of the KSHVI based on a simulation model and its testing.
Practical value The practical results obtained in the dissertation work include: - systems of methods and means for dispatching operational-tactical actions in command control processes; - knowledge base about the main actions of participants in control command control, built and implemented according to the model of expert systems product libraries; - adaptation and configuration of a network versions of the question-answer processor U/K^A to the specifics of information and communication processes of the KSHVI; - a system of methods and means for assessing information flows in the client-server implementation of the KSHVI.
Implementation and implementation For hardware and software support of KSHVI, a software system has been developed, which is based on the client-server implementation of the question-answer processor \VIQA, configured for the command and staff structure of a team of users." The constructed system of simulation models and the developed DUIP were implemented in 726 educational center of the military air defense of the RF Armed Forces to conduct combat air defense operations using a local network in August 2002.
Submitted for defense: 1. A simulation model of the command control unit with a game interpretation of actions as an integrated source of specifications for hardware and software support for the command control unit, taking into account the realities of the exercise time.
2. A set of software tools with a client-server structure, combining methods and tools of simulation modeling, theory and practice of games, expert systems and dispatching systems.
Approbation of the work The main provisions of the dissertation work were reported and discussed at military scientific conferences held at the Military Air Defense Institution of the RF Armed Forces and its branch in the period from 2000 to 2003, at All-Russian scientific and technical conferences. I)1. ANALYSIS OF EXISTING APPROACHES TO CONDUCTING COMPUTER COMMAND STAFF GAMES The level of operational training of the leadership and control bodies of the Russian Armed Forces is one of the important factors determining the degree of readiness of the Armed Forces to solve the tasks assigned to them. Until now, this has been achieved exclusively by traditional methods of organizing and conducting operational training activities.
The introduction of computer forms of operational training into the troop training system represents a logical stage in the further development of existing traditional forms of training, increasing their effectiveness on the basis of scientific and technical achievements of modern computer technology, new methods of mathematical modeling and new information technologies. In the field of domestic CFOP, the main developments belong to specialists from the 27th Central Research Institute of the RF Ministry of Defense and the Higher Air Defense Institution of the RF Armed Forces. In particular, the concept of computer forms of operational training was introduced and justified, and the concepts of their creation and application were formulated. Computer forms of operational training are understood as forms of training for command, operational personnel and university students, which should be based on the use of automated combat simulation systems (ACMS) and the special mathematical and software tools implemented in them. It is important to note here that modeling implies the study of an object, based on its similarity to a model and including building a model, studying it and transferring the obtained information to the modeled object, therefore automated systems for modeling combat operations are a complex of technical, mathematical, information and software tools that ensure decision-making trainees and leadership based on the simulation of combat operations of the warring parties.
The technical basis of such a complex, as a rule, consists of personal computers integrated into a local computer network (LAN).
The area of research will be, based on mathematical modeling, the development of a comprehensive methodology for designing an information process control manager when conducting CSVI.
The effectiveness of the use of CFOP is determined by a qualitatively new organization of ongoing activities based on the integrated use of automated systems and electronic computer technology, software and information tools that provide simulation modeling of the development of combat operations of the warring parties in accordance with the decisions made and a forecast of the possible results of their implementation in a specific combat situation. .
What is fundamentally important in CFOP is that trainees make decisions during the conduct of operations (combat operations) based on the results of modeling the combat operations of the warring parties against the backdrop of a unified operational-strategic situation.
During the CFOP, students acquire skills such as the ability to quickly use computer technology to develop and make decisions when commanding troops (forces), they develop a clear understanding of the role and capabilities of computer technology and automation tools in improving command and control of troops.
In addition, the introduction of CFOP makes it possible to hide the conduct of large-scale games and the general focus of operational training; reduce the damage caused to the environment during combat training activities of troops; eliminate the gap in matters of computerization of operational training of the command staff of our Armed Forces from the armed forces of leading foreign states.
However, the practical implementation of CFOP in the general system of operational and combat training of personnel, including the educational process in universities of the Moscow Region, requires an in-depth analysis of the capabilities of organizing and conducting such forms of training in order to most fully take into account the features of their implementation in both information and technical aspects. The first aspect determines the analysis and assessment of data flows processed during computer games, the second - the possibilities of their technical implementation, including the selection and use of specific technical means.
Before starting to build a simulation model of the KKSHVI, it is important to recall that a game in game theory is a conflict model schematized and adapted for mathematical study. At the same time, of course, a game describing a conflict must preserve all the basic, essential features of the simulated conflict. First of all, the game must reflect the characteristics (“components”) of the conflict: a) the parties involved in the conflict (in game theory they are called players); b) the decisions that players can make (these decisions are usually called player strategies); c) the degree to which each player’s goals are achieved in the situation resulting from the players’ choice of their strategies (these latter characteristics can be measured by numbers called payoffs). An accurate description of the set of players, the set of strategies for each player, as well as their winning functions constitutes the task of the game. Games given in this form are usually called games in normal form.
1.1. ANALYSIS OF THE FEATURES OF ORGANIZING AND CARRYING OUT COMPUTER COMMAND STAFF WAR GAMES Defining the computer form of operational training and in particular the computer command and staff war game as an object of study, it should be noted that in general the structure of computer forms of operational training as a way of organizing the educational process and the structure of traditional forms operational training are similar in principle (Fig. 1.1) and include the following elements: trainees, educational goals and objectives, content and methods of training, leadership apparatus and technical means of training. At the same time, analysis of the content of the structural elements of the circuits presented in Fig. 1.1 allows us to highlight a number of differences between them (Table 1.1.).
The most significant differences are the technical means of training and the associated features of the organization and practical implementation of the educational issues being worked out. The organizational and technical basis of computer forms of operational training are automated systems for modeling combat operations. The use of simulation mathematical modeling tools in ASMBD provides for a change in the methods of organizing and conducting operational training activities and predetermines the features of computer forms of training in general.
The main content of the work of the leadership when conducting computer forms of operational training is the delivery of directives, orders and instructions from the higher command to the participants in the game, escalation of the situation and the execution of military operations, consideration (study) of decisions made, plans of operations (combat actions), directives, (orders) and orders, studying the work methods of trainees using ASMBD tools and special mathematical and software, monitoring the practical actions of headquarters and troops, researching new issues of operational art. The procedure for conveying information about the current situation is fundamentally changing (compared to traditional forms of education). The decisions made by the students are entered into the modeling complex (calculation and modeling subsystem of the ASMBD), the modeling results are displayed through the database (DB) on the workstations of the game participants.
The simulation results are displayed on the automated workplaces of management officials in full for the playing parties, and in the part that concerns the students’ automated workplaces, with subsequent changes in the situation at time intervals equal to the modeling step. At the same time, it is planned to bring the situation to higher authorities, in particular to the command of the armies and the front, only for conditionally active troops: to the command of the armies - for formations and units of army subordination, to the command of the front - respectively, for formations and formations of front-line subordination. The collection of information about the situation from the departments actually operating in the game must be carried out by higher authorities in the prescribed manner along the line of combat control.
Data for the opposite side is provided in a volume corresponding to the capabilities of the forces and means of reconnaissance of the parties, taking into account the decisions of those trained to organize reconnaissance.
The results of the actions of the trainees and the development of the situation during the CFOP must be recorded. Recording the actions of officials, recording the development of the situation from the moment the warring parties receive combat missions until the completion of their implementation will contribute to a significant increase in the responsibility of officials for their actions and the desire to work with full dedication. Keeping a protocol will also ensure objectivity in assessing the actions of students when summing up the results, and will significantly simplify the work of the management staff when preparing the analysis of the game.
Management apparatus Learning environment Methods of creating a learning environment Introducing students into a learning environment Acting out the situation Designation Imitation Natural modeling of the environment Attracted forces and means Exercise development groups Intermediaries and acting out groups; communication facilities of the Simulation Group; imitation means Real troops, forces and means Trained control bodies a) Management apparatus Training environment Methods of creating a learning environment Introducing trainees into a training environment Acting out the situation Simulation modeling of the situation Attracted forces and means Exercise development group Computer center ASMBD Playing groups Trained controls b) Fig. 1.1. Structural diagram of the implementation of forms of operational training: a) traditional; b) computer.
Table 1.1 Distinctive features of elements of computer forms of operational training from traditional ones Elements of structures Distinctive features Trainees When conducting CFOP, trainees are required to have skills and abilities in working with automation tools. Trainees get the opportunity to make decisions and analyze them based on multivariate simulation of combat operations.
Educational Objectives It becomes possible to objectively monitor the knowledge, skills and abilities of students. Learning goals can be achieved in a shorter time through the use of training programs.
Training methods Mathematical modeling of combat operations will be the basis of the methodology of computer forms of operational training and will provide the leadership apparatus with: increasing the dynamism of the build-up of the situation and conducting combat operations in real time using the “free” game method; expanding the range of methodological techniques used; replay of individual episodes of hostilities in an accelerated time mode, stopping operational time to analyze decisions made and showing an alternative solution with identifying its advantages, documenting and post-game reproduction of the course and results of the actions of troops (forces), etc.; qualitative analysis and objective assessment of decisions made by students.
Management Apparatus The presence of automated combat simulation systems (ACMS) predetermines the need to include in the management apparatus officials who ensure the functioning of ACMMS. The composition of the situation building groups (playing along groups) is being reduced, and the functional responsibilities of intermediaries are fundamentally changing.
Technical means of training The organizational and technical basis of the CFOP is an automated system for modeling combat operations, the use of which radically changes the methods of preparing and conducting operational training activities and predetermines the features of the CFOP as a whole.
In general, the block diagram of the complex of technical and software tools that ensure the organization and conduct of computer-based monitoring tests is shown in Fig. 1.2.
As noted earlier, the main component of such a complex of technical and software tools is an automated combat simulation system, which is a complex organizational and hierarchical system that includes complexes of technical, mathematical, software and information tools.
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Conclusion of the dissertation on the topic “Mathematical modeling, numerical methods and software packages”, Yampolsky, Leonid Semenovich
CONCLUSION MAIN RESULTS OF THE WORK
An analysis of existing approaches to conducting computer-based monitoring tests, as well as existing methodological and instrumental means for managing information exchange and dispatching information processes, was carried out. As a result of the research, the following results were obtained:
1. A simulation model of command and control units has been developed and studied, based on their game interpretation, which emphasizes the place and role of air defense in their protective function.
2. A computer support system for collective actions of KSHVI participants has been developed, providing management and communication within the framework of the command and staff organizational structure.
3. The KSHVI simulation model was used as a source of specifications, on the basis of which the WIQA question-answer processor was selected as the basic instrumental environment for the implementation of KSHVI.
4. Adaptation and settings of the WIQA question-answer processor to the specifics of the studied version of the KSHVI were carried out and the place and role of the KSHVI dispatcher in the instrumental environment were determined.
5. An analysis of the information processes occurring during computer-based monitoring tests was carried out. A formal description of information processes was carried out, which made it possible to determine the possibilities for managing them and distribute management functions between the created dispatcher and the means of the operating systems and network technologies used.
6. A methodology has been developed for assessing the effectiveness of information process management when conducting computer-based monitoring tests. The concept of the effectiveness of information process management and aspects of their implementation in relation to which the specified assessment should be carried out is substantiated.
7. Based on the scientific and methodological apparatus proposed in the work, a prototype of an information process management manager has been developed. On its basis, experimental studies were carried out on managing information processes and assessing its effectiveness. The experiment fully confirmed the theoretical provisions of the developed scientific and methodological apparatus for designing an information process management manager and assessing management effectiveness.
8. The developed scientific and methodological apparatus provides a qualitatively new solution to the problem of designing means of managing information processes in relation to the specifics of their flow during computer-based computer monitoring.
The resulting solution to this problem is common to the class of problems of developing means of controlling information processes during computer command and control operations at all levels of military air defense.
The obtained results of the work are proposed to be used to solve scientific and technical problems of designing information process control tools when organizing specific computer control systems.
List of references for dissertation research Candidate of Technical Sciences Yampolsky, Leonid Semenovich, 2003
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41. Ventzel E. S. Theory of Probability. M., Science. 1969.1. LIST OF ABBREVIATIONS
42. API Application Programming Interface (application programming interface)
43. MOM Message Oriented Middleware
44. ORB Object Request Broker (object request broker)
45. OSI Open System Interconnection (open systems interaction)
46. RPC Remote Procedure Call
47. ADF data transmission equipment
48. Workstation automated workstation
49. ASMBD automated combat simulation system
50. ACS automated control system
51. ASUV automated troop control system1. DB database1. Sun computing system
52. SAM anti-aircraft missile system
53. Anti-aircraft missile system
54. KKShU computer command and staff exercises
55. KSA complex of automation equipment
56. KFOP computer forms of operational training
57. Command staff exercises
58. LAN local area network1. OS operating system
59. Air defense air defense
60. Software software
61. Software middleware1. PC personal computer
62. Airborne attack means
63. SMPO special mathematical and software
64. DBMS database management system
Please note that the scientific texts presented above are posted for informational purposes only and were obtained through original dissertation text recognition (OCR). Therefore, they may contain errors associated with imperfect recognition algorithms. There are no such errors in the PDF files of dissertations and abstracts that we deliver.
MILITARY THOUGHT No. 7/2009, pp. 12-20
Simulation of armed confrontation: development prospects
Colonel IN AND. GRAZING,
candidate of military sciences
Colonel D.B. KALINOVSKY
Colonel O. V. TIKHANYCHEV,
Candidate of Technical Sciences
AT THE PRESENT, the role and importance of military-scientific substantiation of decisions of state and military command and control bodies in the field of construction, training, planning the use and management of the Armed Forces is significantly increasing in the course of solving the tasks facing them to ensure the military security of the state. At the same time, as the experience of local wars and armed conflicts shows, the most important conditions for successfully achieving the goals of modern operations are timely tracking and display in near real time of the situation in conflict zones, forecasting its development, elaboration of various options for actions of the troops of the parties, including including using mathematical modeling methods.
The relevance of the problem of applying mathematical modeling methods in military affairs is confirmed by a large number of publications on this topic in various periodicals. Their analysis shows that the opinions of the authors vary, ranging from complete rejection of mathematical models in military affairs to a completely objective understanding of this issue, although with certain reservations.
The reasons for this range of opinions are varied. Some believe that calculation tasks and a mathematical apparatus for comparing combat potentials are sufficient for information support of operation planning; others insist on the use of simplified models, relying on the commander’s ability to “build a mental model of the upcoming battle and operation,” or simply do not distinguish between models and calculation problems, interpreting their definitions quite freely.
Although almost all authors talk about the need for forecasting in the work of commanders (commanders) and staffs, very often there is an opinion, confirmed, at first glance, by well-founded examples and reasoning, that the use of mathematical modeling methods is inappropriate and sometimes dangerous, since it leads to a distortion of the assessment planning results. In our opinion, there are several reasons for this misconception. This is, firstly, a lack of understanding of the essence of mathematical modeling, the purpose of the models used, their capabilities, the assumptions taken when developing and the boundaries of application. Secondly, putting forward the same operational and technical requirements for models and tasks for various purposes, used for different levels of management. And finally, thirdly, the unreasonable “absolutization” of the modeling results.
All this is a consequence of different understandings of the problem of modeling armed confrontation by military theorists and officials of military command and control agencies. To discuss this issue reasonably, First of all, you need to decide on its main components: terminology of mathematical modeling; classification of mathematical models and forecasting methods; methodology and boundaries of application of mathematical models; technologies for implementing mathematical models for various purposes.
First of all, you should understand what to count mathematical model(MM) what information and calculation task(IRZ), and also how it differs math modeling from carrying out operational-tactical calculations(OTR). In the reference literature there is a fairly large number of definitions of the concepts under consideration.
So, in the “Military Encyclopedia” mathematical model is interpreted as a description of a phenomenon (object) using mathematical symbols. In the "Military Encyclopedic Dictionary" math modeling in military affairs it is formulated as a method of military-theoretical or military-technical research of an object (phenomenon, system, process) by creating and studying its analogue (model) in order to obtain information about the real system.
Operational-tactical calculations in the same dictionary are described as calculations carried out by the personnel of departments, formations, formations, units and subunits, the purpose of which is to determine quantitative, qualitative, time and other indicators for making decisions on an operation (battle) or justifying planning for the use of troops and ensuring control.
One of the most popular electronic Internet encyclopedias, Wikipedia, gives its formulations of concepts related to mathematical modeling. So, task in the most general “canonical” form - a logical statement like: “given given conditions, it is required to ensure the achievement of a certain goal,” and model - a logical or mathematical description of components and functions that reflect the essential properties of the object or process being modeled.
Based on the definitions given in the same source, one can clearly see the significant difference between an individual mathematical model, a complex and a system of models. Set of models - a set of models designed to solve one complex problem, each of which describes one or another aspect of the modeled object or process. If the models are connected in such a way that the results of some turn out to be the initial data for others before obtaining a common result, then the complex turns into a system of models. Model system - a set of mutually related mathematical models to describe complex systems that cannot be reproduced in one model. To plan and predict the behavior of large objects, systems of models are developed, usually built on a hierarchical principle, V several levels. They are called multi-level systems.
And finally, the current GOST series “RV” provides the following definitions of the mathematical model and calculation problem. Mathematical model of operation (combat)- a system of mathematical dependencies and logical rules that allows one to reproduce in time the most significant components of simulated combat operations with sufficient completeness and accuracy and, on the basis of this, calculate the numerical values of the indicators of the predicted course and outcome of military operations.
Calculation problem - a set of mathematical dependencies, algorithms and data for performing operational-strategic (operational-tactical) or special calculations, allowing one to assess the situation that will arise as a result of the proposed actions or calculate control parameters that ensure the achievement of the required result with a probability not lower than the specified one.
Analysis of these definitions shows the difference between MM and IRD, which consists in the fact that the former are intended to predict the development of the situation under different variants of the initial data, and the latter are primarily intended to carry out direct calculations in the interests of obtaining a specific result. Earlier IRZ were solved mainly by hand, and MM- on “mainstream” computers. With the development of automation tools, many tasks were transferred in the form of programs to COMPUTER, which made it possible to complicate the mathematical apparatus used, the number of factors taken into account, and led to some “blurring” of the line between MM and IRD. This, in our opinion, is one of the reasons for misunderstandings regarding the use of mathematical modeling in the course of operational-tactical calculations.
In accordance with the governing documents, the main functions of headquarters are collecting information and assessing it, planning an operation (battle) and forecasting changes in the situation. With planning, everything is quite clear: it primarily involves solving direct and reverse IRDs. But to assess the situation, predict its changes, as well as for a comparative assessment of the planned options for the use of troops (forces), the use of various mathematical forecasting methods is required (Fig.).
Classification of forecasting methods
Each of these methods has been tested in various areas of management activity and has proven its right to exist. But not all of them can be used in the practical activities of commanders (commanders) and staffs when organizing military operations. This is due to the peculiarities of warfare, which consist in the significant uncertainty of the initial data, the need to take into account a huge number of factors and the high “cost” of erroneous decisions. Based on this, methods of extrapolation of trends and some types of models are almost never used in organizing military operations. Expert methods and mathematical modeling are a different matter, but their application is also significantly influenced by the above features.
Formally, any of the approaches to forecasting shown in the figure can be attributed to modeling processes and identifying trends: logical, mental, mathematical. But based on the specifics of modeling armed confrontation, the definition of MM used in GOSTs of the “RV” series, it is advisable, when talking about modeling, to consider mathematical models that describe the processes of armed confrontation, its components and individual forms. Below we will talk mainly about such models.
The classification of mathematical models affects the requirements for them, the formation of lists of MM and IRZ, which provide decision support for officials of military command and control agencies. According to their purpose, MMs are usually divided into research and staff (Table 1).
Table 1
Classification of mathematical models
Research models are intended both to support research related to the development of weapons, the development of new methods of conducting operations and combat operations, and to analyze the results of calculations during advance planning. The main requirement for them is to ensure the necessary accuracy of the mathematical description of the processes under study. Less stringent requirements are imposed on the efficiency of modeling.
Staff models are mathematical models of operations (combat actions) designed to support the practical activities of headquarters. They are presented with two basic requirements: first - the possibility of application in real time, fitting into the algorithm of the headquarters; the second is to ensure a significant increase in the objectivity and validity of decisions made regarding the command and control of troops.
According to the form of description of the process of armed confrontation, MM are divided into analytical And stochastic. Both of them can be both staff and research.
According to the obtained modeling result, the models are most significantly divided into straight(describing) and prescriptive(optimizing or prescriptive). The first ones allow you to answer the question: “what will happen if...”, the second ones: “how to make it happen like this.” Descriptive models are most often used in military affairs. The use of prescriptive models, which are more promising from the point of view of decision support, is hampered by a number of objective and subjective factors.
Objective is that with a large number of factors taken into account, it is very difficult to formulate a formal problem of finding an optimal solution. It is equally difficult to interpret the results obtained. Subjective factors: the reluctance of officials to trust the search for a solution to a program whose operating principles are unknown to them. There is also an opinion that the algorithm of the prescriptive model can be calculated, and, knowing it, the result of the decision can be calculated. This opinion is undoubtedly erroneous, since even with a known algorithm for the model’s operation, it is impossible to calculate the result of the simulation without having accurate information about the initial data entered into the model.
It is difficult to judge how significant these factors are for the development of MM, but the fact is clear: currently for forecasting in the military field, descriptive models are used. This trend is likely to continue in the near future.
Some sources, discussed at the beginning of the article, express the opinion that modeling (and sometimes forecasting) can be replaced by direct calculations; it is enough to describe the process with a varying degree of approximation by a system of equations. However, there is a subtle but dangerous pitfall in this approach. Firstly, some processes are simply impossible to describe explicitly. Secondly, describing the behavior of a system with equations in explicit form requires the introduction of a significant number of correction and generalizing coefficients, most of which are obtained empirically by generalizing the statistics of known events. This is done under strictly specified conditions, which the potential user of the settlement system will not know about at the time of making the decision. Any change in the forms, methods, or means of armed struggle reduces the accuracy of the system of equations and distorts the solution of the problem. That's why Calculation methods will never replace a model operating with probabilistic approaches.
The boundaries of the application of mathematical modeling, the list of applied MMs within the framework of the above classification are determined by the forecasting and assessment problems solved in the military command and control bodies using them, as well as the capabilities of providing input and the needs for output information of the models. From the analysis of the requirements of the main governing documents and the experience of operational training activities, it is possible to determine the needs of military command and control bodies in the use of mathematical models and present their hierarchical structure (Table 2).
The proposed classification is not a dogma, but only reflects the needs of military command and control bodies for means of calculation and information (in the long term and intellectual) support and justification of decisions made. The implementation of the proposed models at management levels, their multi-link interconnection, is essentially the prospect for the development of mathematical modeling.
Despite the objective need to use mathematical models in organizing military operations, their use is significantly influenced by subjective factors associated with the attitude of officials to the modeling results. It should be clearly understood that the model is not a means of directly developing decisions on the use of troops (forces) or justifying ways of developing a weapons system, but only a tool that ensures the implementation of one of the stages of this process - a comparative assessment of the quality of decisions made. This tool is developed for specific tasks and conditions with certain assumptions and has a corresponding scope. Moreover, it is not always possible and necessary to develop a certain universal model; it is often more expedient to have a set of tools used to solve specific problems at certain workplaces (management levels), adapted to specific working conditions. Only such an understanding will make it possible to formulate the correct approach to the use of model technologies in military command and control agencies and bring the organization of military operations (operations, combat actions) of the RF Armed Forces to a qualitatively new level that meets the requirements of modern warfare.
In this regard, as well as from the point of view of the technological implementation of model technologies, the most appropriate classification of mathematical models regarding their inclusion in the special mathematical and software (SMPO) of automated troop control systems (ATCS) seems to be most appropriate. With this approach, models can be implemented, firstly, directly as part of the SMPO automation equipment complexes(KSA) ACCS; secondly - in the form of separate software and hardware systems(PTK), providing solutions to specific problems; thirdly - as part of stationary or mobile multifunctional modeling centers(computer centers for modeling military operations - CC MIA).
Experience in the development and operation of automated control systems shows that in a number of cases there is the objective need to include mathematical models in the SMPO ASUV, for example, to provide a comparative analysis of options for the use of troops when developing an operation plan, assessing the effectiveness of options for constructing a massive fire strike, etc. Mathematical models operating as part of special software (SPO) of the automated control system must ensure automated exchange of information with the system database, other models and tasks, receiving most of the information from them in an automated manner. These models must have an extremely simple user interface that provides a sufficient set of formalized control actions for the order of use of troops (forces) and combat systems, as well as functions for a visual presentation of modeling results.
table 2
Hierarchical structure of mathematical models of armed
confrontation
We are talking primarily about staff models, sometimes also called “express models” in the specialized literature, although the definition of “express” sounds somewhat pejorative, reflecting only the external consumer qualities of the model - ease of control and speed of obtaining results. At the same time, staff models are quite complex products: they adequately describe the process for which they were developed to model. External simplicity is achieved through long-term work on optimizing computational algorithms and user interfaces. But it is precisely these models that can be widely used by officers who do not have special computer training.
To be fair, it should be noted that creative and “piecemeal” work on creating program interfaces and developing approaches to unify them, which can only be performed by a specialist with a broad operational and technical outlook, does not belong to scientific activity. At the same time, the lack of unified approaches to the interface implementation of mathematical models and information and calculation tasks in the work of officials significantly reduces their user properties, making it difficult for officials to master and implement them in the activities of military command and control bodies.
Models that are more diverse in functionality, although more complex to operate, are sometimes advisable not to be included in the ACS V SMPO, but to be used as part of multifunctional computer modeling centers or separate specialized hardware systems. This is due to the following factors:
complex models, complexes and systems of models can form computer requirements, not always provided by means of serial automated control systems;
the high cost of development and the need to maintain complex mathematical models sometimes makes it impractical to supply them to military command authorities for use only a few times a year, and sometimes less often, it is more expedient use one model in move mode as part of mobile hardware systems with its own personnel;
more complex and diverse models require maintenance more trained specialists, which are not always available in automated military command and control bodies;
requirements for the composition and detail of the initial data of complex models (complexes and systems of models) do not always allow them to be organized automated interaction with the ACCS database;
variety of output information requires it comprehensive assessment, often bordering on science and art, which can only be achieved by an experienced modeling specialist. Moreover, only a specialist in the field of modeling can know in detail the assumptions and limitations adopted during the development of the model, the scope of its application and assess the degree of influence of these factors on the modeling results. In the matter of operational (combat) planning, given the high cost of a mistake, this is an important circumstance.
These factors, combined with the need to ensure solutions to the problems of operational planning and the formation of a weapons program, necessitate the creation of specialized computer centers (separate PTCs) for modeling military operations (CC MVD) outside the framework of the automated control system. Such computer simulation centers can be stationary or mobile, equipped with computers in various configurations, but at the same time, the conditions for the possibility of exchanging information between the CC of the Ministry of Internal Affairs and the automated control system and ensuring the requirements for the safety of the initial information of the automated control system must be met.
Stationary modeling centers can be used in the interests of senior management bodies when carrying out strategic planning, organizing and analyzing the results of operational training activities, forming weapons programs, developing mobilization plans and carrying out other similar activities.
Mobile CCs of the Ministry of Internal Affairs can be used to strengthen the headquarters of operational-strategic and operational units during operational planning and advance preparation of operations, as well as during operational (combat) training activities.
Thus, mathematical modeling in the field of armed confrontation is advisable, in our opinion sight, develop in the following main areas:
First - creation of staff models that take into account the main factors influencing the process of confrontation, with an extremely simple interface for use as part of the automated control system software when conducting a comparative assessment of decisions on the use of troops (forces). Along with this, it is possible to consider the possibility of introducing models into calculation and modeling complexes in order to conduct a comparative assessment of the calculated options automatically, unnoticed by the user.
Second - creation of specialized hardware systems, including mobile ones, interfaced with automated control system automated control system for input and output data, for modeling in the interests of solving complex problems and problems with limited access to information.
Third - creation outside the framework of automated control systems of multifunctional control centers of the Ministry of Internal Affairs, including complexes and systems of mathematical models and calculation problems in order to ensure the solution of a wide range of problems of assessing and forecasting the situation in the interests of making military-political decisions, planning military operations and building the Armed Forces.
The proposed classification of models, the proposed conceptual apparatus and approaches to the implementation of MM for military command and control bodies at various levels will allow, in our opinion, to clearly define the place and principles of using mathematical modeling technologies in the RF Armed Forces, to develop a unified view on the methods of using MM in the construction system, planning application , training and command and control of troops (forces), streamline the process of their development and implementation into practice of the activities of military command and control bodies.
An analysis of the state, prospects for the development of modeling and the dynamics of growth in costs for the development of mathematical models of military operations in the armed forces of the leading states of the world shows the seriousness of this issue abroad and serves as additional confirmation of the relevance of the issues discussed in this article.
Military Thought. 2004. No. 10. P. 21-27; 2003. No. 10. P. 71-73.
Military Thought. 2007. No. 9. P. 13-16; 2007. No. 10. P. 61-67; 2008. No. 1. P. 57-62.
Military Thought. 2005. No. 7. P. 9-11; 2006. No. 12 P. 16-20.
Military Thought. 2007. No. 10. P. 61-67; 2007. No. 9. P. 13-16; 2008. No. 3. P. 70-75.
Military Encyclopedia. M.: Voenizdat, 2001. T. 5. P. 32.
Military encyclopedic dictionary. M.: RF Ministry of Defense, Institute of Military History, 2002. P. 1664.
http://www.wikipedia.org._
Foreign military review. 2006. No. 6. P. 17-23; 2008. No. 11. P. 27-32.
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The process of creating mathematical models of combat operations is labor-intensive, lengthy and requires the use of specialists of a sufficiently high level who have good training both in the subject area related to the object of modeling and in the field of applied mathematics, modern mathematical methods, programming, who know the capabilities and specifics of modern computing. technology. A distinctive feature of the mathematical models of combat operations currently being created is their complexity, due to the complexity of the objects being modeled. The need to build such models requires the development of a system of rules and approaches that can reduce the cost of model development and reduce the likelihood of errors that are difficult to eliminate later. An important component of such a system of rules are the rules that ensure the correct transition from a conceptual to a formalized description of the system in a particular mathematical language, which is achieved by choosing a specific mathematical scheme. A mathematical scheme is understood as a particular mathematical model for converting signals and information of a certain element of a system, defined within the framework of a specific mathematical apparatus and aimed at constructing a modeling algorithm for a given class of elements of a complex system.
In the interests of a reasonable choice of a mathematical scheme when constructing a model, it is advisable to classify it according to the purpose of modeling, method of implementation, type of internal structure, complexity of the modeling object, and method of representing time.
It should be noted that the choice of classification criteria is determined by the specific objectives of the study. The purpose of classification in this case is, on the one hand, a reasonable choice of a mathematical scheme for describing the process of combat operations and its representation in a model in the interests of obtaining reliable results, and on the other hand, identifying the features of the simulated process that must be taken into account.
The purpose of the simulation is to study the dynamics of the armed struggle process and evaluate the effectiveness of combat operations. Such indicators are understood as a numerical measure of the degree of completion of a combat mission, which can be quantitatively represented, for example, by the relative amount of prevented damage to defense facilities or damage inflicted on the enemy.
The method of implementation should consist of a formalized description of the logic of functioning of weapons and military equipment (WME) in accordance with their analogues in the actual process. It must be taken into account that modern weapons and military equipment are complex technical systems that solve a set of interrelated problems, which are also complex technical systems. When modeling such objects, it is advisable to preserve and reflect both the natural composition and structure, as well as the algorithms for the combat functioning of the model. Moreover, depending on the purposes of modeling, it may be necessary to vary these model parameters (composition, structure, algorithms) for different calculation options. This requirement determines the need to develop a model of a specific sample of weapons and military equipment as a composite model of its subsystems, represented by interconnected components.
Thus, according to the classification criterion, the type of internal structure, the model must be composite and multi-component, and according to the method of implementation, it must provide simulation modeling of combat operations.
Complexity of the modeling object. When developing components that determine the composition of models of weapons and military equipment, and combining models of weapons and military equipment into a single model of combat operations, it is necessary to take into account the characteristic scales of time averaging of quantities appearing in the components that differ by orders of magnitude.
The ultimate goal of modeling is to evaluate the effectiveness of combat operations. It is to calculate these indicators that a model is being developed that reproduces the process of combat operations, which we will conditionally call the main one. The characteristic time scale of all other processes included in it (primary processing of radar information, target tracking, missile guidance, etc.) is much less than the main one. Thus, it is advisable to divide all processes occurring in armed struggle into slow ones, the development forecast of which is of interest, and fast ones, the characteristics of which are not of interest, but their influence on the slow ones must be taken into account. In such cases, the characteristic time scale of averaging is chosen so as to be able to construct a model of the development of the main processes. As for fast processes, within the framework of the created model, an algorithm is needed that allows, at the moments of fast processes, to take into account their influence on slow ones.
There are two possible approaches to modeling the influence of fast processes on slow ones. The first is to develop a model of their development with a corresponding characteristic time scale of averaging, much smaller than that of the main processes. When calculating the development of a fast process in accordance with its model, the characteristics of slow processes do not change. The result of the calculation is a change in the characteristics of slow processes, which, from the point of view of slow time, occurs instantly. In order to be able to implement this method of calculating the influence of fast processes on slow ones, it is necessary to introduce the corresponding external quantities, identify and verify their models, which complicates all stages of the modeling technology.
The second approach consists in abandoning the description of the development of fast processes using models and considering their characteristics as random variables. To implement this method, it is necessary to have distribution functions of random variables that characterize the influence of fast processes on slow ones, as well as an algorithm that determines the moments of the onset of fast processes. Instead of calculating the development of fast processes, a random number is thrown out and, depending on the dropped value, in accordance with the known distribution functions of random variables, the value that the dependent indicators of slow processes will take is determined, thus taking into account the influence of fast processes on slow ones. As a result, the characteristics of slow processes also become random variables.
It should be noted that with the first method of modeling the influence of fast processes on slow ones, the fast process becomes slow, the main one, and its course is influenced by processes that are already fast in relation to it. This hierarchical nesting of fast processes into slow ones is one of the components of the quality of modeling the process of armed struggle, which classifies the model of combat operations as structurally complex.
Method of representing model time. In practice, three concepts of time are used: physical, model and processor. Physical time refers to the process being modeled, model time refers to the reproduction of physical time in the model, processor time refers to the execution time of the model on a computer. The ratio of physical and model time is specified by the coefficient K, which determines the range of physical time taken as a unit of model time.
Due to the discrete nature of the interaction of weapons and military equipment samples and their representation in the form of a computer model, it is advisable to set the model time by incrementing discrete time intervals. In this case, two options for its representation are possible: 1) discrete time is a sequence of real numbers equidistant from each other; 2) the sequence of time points is determined by significant events occurring in the simulated objects (event time). From the point of view of computing resources, the second option is more rational, since it allows you to activate an object and simulate its operation only when a certain event occurs, and in the interval between events, assume that the state of the objects remains unchanged.
One of the main tasks when developing a model is to fulfill the requirement of synchronization of all simulated objects in time, that is, the correct mapping of the order and temporal relationships between changes in the process of combat operations on the order of events in the model. With a continuous representation of time, it is believed that there is a single clock for all objects that shows the same time. The transfer of information between objects occurs instantly, and thus, by checking with a single clock, it is possible to establish the time sequence of all events that took place. If there are objects in the model with a discrete representation of time, in order to form a single model clock, it is necessary to combine many time samples of object models, order and define the values of grid functions on the missing time samples. It is possible to synchronize object models with event time only explicitly, by transmitting a signal about the occurrence of an event. In this case, a control program-scheduler is needed for organizing the execution of events of various objects, which determines the required chronological order of event execution.
In a combat model, it is necessary to use event and discrete time together; this representation of time is called hybrid. When using it, the simulated objects acquire the property of changing the values of some state indicators abruptly and almost instantly, that is, they become objects with hybrid behavior.
To summarize the above classification, we can conclude that the combat action model should be a composite, structurally complex, multi-component, dynamic, simulation model with hybrid behavior.
For a formalized description of such a model, it is advisable to use a mathematical scheme based on hybrid automata. In this case, samples of weapons and military equipment are represented as multicomponent active dynamic objects. Components are described by a set of state variables (external and internal), structure (single-level or hierarchical) and behavior (behavior map). Interaction between components is carried out by sending messages. To combine components into a model of an active dynamic object, the rules of composition of hybrid automata are used.
Let us introduce the following notation:
sÎRn - vector of object state variables, which is determined by a set of input influences on the object, influences of the external environment 
, internal (own) parameters of the object hkÎHk,;
A set of vector functions that determine the law of operation of an object in time (reflect its dynamic properties) and ensure the existence and uniqueness of the solution s(t);
S0 is the set of initial conditions, including all the initial conditions of the object components generated by the initialization function during operation;
A predicate that determines a change in the behavior of an object (selects the desired one from all specially selected states, checks the conditions that should accompany the event, and takes the value true when they are fulfilled) is specified by a set of Boolean functions;
An invariant that defines a certain property of an object that must be preserved over specified periods of time is specified by a set of Boolean functions;
- a set of real initialization functions that assign the value of the solution at the right end point of the current time interval to the value of the initial conditions at the left starting point at the new time interval: s()=init(s());
Hybrid time is specified by a sequence of time intervals of the form , - closed intervals.
The hybrid time elements Pre_gapi, Post_gapi are the “time gap” of the next step of the hybrid time tH=(t1, t2,…). At each clock cycle on segments of local continuous time, the hybrid system behaves like a classical dynamic system until the point t*, at which the predicate that determines the change in behavior becomes true. Point t* is the end point of the current and the beginning of the next interval. The interval contains two time slots in which state variables can change. The flow of hybrid time in the next clock cycle ti=(Pre_gapi,, Post_gapi) begins with the calculation of new initial conditions in the time slot Pre_gapi. After calculating the initial conditions, the predicate is checked at the left end of the new time interval. If the predicate evaluates to true, the transition is made immediately to the second time slot, otherwise a discrete sequence of actions corresponding to the current time step is performed. The Post_gapi time slot is designed to perform instant actions after the completion of long-term behavior at a given hybrid time step.
By hybrid system H we mean a mathematical object of the form
.
The modeling task is to find a sequence of solutions Ht=((s0(t),t, t0), (s1(t),t,t1),…), defining the trajectory of the hybrid system in the phase space of states. To find the sequence of solutions Ht, it is necessary to conduct an experiment or simulation on a model with given initial data. In other words, unlike analytical models, with the help of which a solution is obtained using known mathematical methods, in this case it is necessary to run a simulation model, and not a solution. This means that simulation models do not formulate their solution in the same way as is the case when using analytical models, but are a means and source of information for analyzing the behavior of real systems in specific conditions and making decisions regarding their effectiveness.
In the 2nd Central Research Institute of the Ministry of Defense of the Russian Federation (Tver), based on the representation of simulated objects in the form of hybrid automatic machines, a simulation modeling complex (IMK) “Seliger” was developed, designed to assess the effectiveness of groupings of forces and means of aerospace defense when repelling attacks from aerospace weapons. th attack (SVKN). The basis of the complex is a system of simulation models of objects, simulating algorithms for the combat functioning of real weapons and military equipment (anti-aircraft missile system, radar station, command post automation system (for radio engineering troops - radar company, battalion, brigade, for anti-aircraft missile forces - regiment, brigade etc.), combat aviation complex (fighter aircraft and aerospace attack weapons), electronic suppression equipment, non-strategic missile defense fire systems, etc.). Models of objects are presented in the form of active dynamic objects (ADO), which include components that make it possible to study the dynamics of various processes during their functioning.
For example, a radar station (radar) is represented by the following components (Fig. 1): antenna system (AS), radio transmitting device (RPrdU), radio receiving device (RPru), passive and active interference protection subsystem (PZPAP), primary information processing unit ( POI), secondary information processing unit (SOI), data transmission equipment (ADT), etc.
The composition of these components as part of the radar model makes it possible to adequately simulate the processes of receiving and transmitting signals, detecting echo signals and bearings, noise protection algorithms, measuring signal parameters, etc. As a result of the modeling, the main indicators are calculated that characterize the quality of the radar as a source of radar information (detection zone parameters, accuracy characteristics, resolution, performance, noise immunity, etc.), which makes it possible to evaluate the effectiveness of its operation under various conditions of the target noise environment.
Synchronization of all simulated objects in time, that is, the correct mapping of the order and temporal relationships between changes in the process of combat operations to the order of events in the model, is carried out by the object management program (Fig. 2). The functions of this program also include creating and deleting objects, organizing interaction between objects, and logging all events occurring in the model.
The use of an event log allows for a retrospective analysis of the dynamics of combat operations with any simulated object. This makes it possible to assess the degree of adequacy of object models both using limit point methods and by monitoring the correctness of modeling processes in the components of an object (that is, checking the adequacy by running from input to output), which increases the reliability and validity of the results obtained.
It should be noted that the multicomponent approach allows you to vary their composition (for example, to study the combat operation of air defense systems with different types of automated control systems) in the interests of synthesizing a structure that satisfies certain requirements. Moreover, due to the typing of the program representation of the components, without reprogramming the source code of the program.
The general advantage of this approach when building a model is the ability to quickly solve a number of research problems: assessing the impact of changes in the composition and structure of the control system (number of levels, control cycle, etc.) on the effectiveness of the combat operations of the group as a whole; assessment of the influence of various information support options on the potential combat capabilities of the samples and the group as a whole, research of forms and methods of combat use of the samples, etc.
The model of combat operations built on the basis of hybrid automata is a superposition of joint behavior of parallel and/or sequentially functioning and interacting multi-component ADOs, which are a composition of hybrid automata operating in hybrid time and interacting through message-based connections.
Literature
1. Sirota A.A. Computer modeling and efficiency assessment of complex systems. M.: Tekhnosphere, 2006.
2. Kolesov Yu.B., Senichenkov Yu.B. Systems modeling. Dynamic and hybrid systems. St. Petersburg: BHV-Petersburg, 2006.
FOREIGN MILITARY REVIEW No. 11/2008, pp. 27-32
US Army JWARS
Captain 1st rankN . REZYAPOV ,
major S. CHESNOKOV ,
captain M. INYUKHIN
Computer modeling has been firmly established in the arsenal of tools at all levels of the leadership of the US Armed Forces for quite some time now. Since the early 2000s, the US military leadership has identified means of simulating and modeling combat operations as a priority technology in the formation of military-technical policy. The high dynamics of development of computer technology, programming technologies, and system-technical foundations for modeling various real processes have marked a huge breakthrough in the United States in the development of models and simulation systems.
The main directions of development of modeling in the US Armed Forces are: optimization of the structure of the Armed Forces, development of concepts for the combat use of troops (forces), development of tactics and operational art, optimization of the process of acquiring new types of weapons and military equipment, improvement of operational and combat training, etc. At the same time, recent emphasis has been placed on to create systems and models aimed at solving problems in the field of construction and use of joint and coalition groupings of troops (forces). An example is the joint combat simulation system JWARS (Joint Warfare System), which is a model of conducting military operations by joint groups of troops. It allows you to simulate ground, air, sea operations and combat operations, the actions of special and information operations forces, the protection / use of chemical weapons, the actions of missile defense / air defense systems in theaters, control and space reconnaissance, communications, and logistics support.
JWARS is a modern structural modeling system developed using CASE (computer-aided software engineering) tools in the Smalltalk programming language. It uses event time and simulates the activities and interactions of military units. Within the framework of this system, the issues of creating a three-dimensional virtual combat space, taking into account weather conditions and terrain features, logistics support for combat operations, creating a clear system of information flows, as well as issues of decision support in the command and control system have been worked out quite deeply.
The main purpose of JWARS is to simulate combat operations of joint operational formations (JFO), which should improve the quality of joint operational planning and use of armed forces, assess the combat capabilities of joint formations and develop conceptual documents for the construction of the armed forces as a whole.
This system allows for comprehensive control of the process of operational planning and execution, as well as repeated testing of the same tasks, which significantly increases the ability to analyze the results of ongoing actions and select the most effective scenario for the use of forces and means.
PossibilitiesJWARS:
- allows you to plan military operations lasting more than 100 days;
- simulation time scale 1:1000 (1,000 times faster than real time);
- model initialization time up to 3 minutes.
The development of the model is carried out under the direct supervision of the head of the department for analysis and evaluation of programs. The importance of JWARS for the development and testing of promising strategic concepts, the development of forms and methods of combat use of joint forces in the conditions of network-centric combat operations is emphasized.
The latest version of JWARS is distinguished by the presence of a modular system for modeling a network of inter-theater military transportation, an improved simulation window for the command and control system of the military unit, the ability to simulate attacks on mobile targets, the presence of a geoinformation and geophysical database for Southeast Asia, the Far East, South Asia and South America, and increased performance due to the modernization of the program code and the introduction of a new technical base, the ability to construct a script, etc.
WMD simulations currently include simulating defense against chemical weapons and assessing their impact on combat units and the environment. In the near future, it is planned to create modeling blocks for assessing the use of biological and nuclear weapons.
The Air Force action model supports the solution of about 20 types of typical tasks. Describes the processes of direct air support, the use of missile systems, the delivery of massive missile and air strikes (MRAU), providing air defense to combat areas, the destruction of ground/air/sea targets, the suppression of the enemy's air defense system, the massive use of UAVs, target designation and guidance under time constraints , laying mines from air carriers, in-flight refueling, etc.
The Navy action model contains the processes of hitting surface targets, using submarines against surface forces, naval blockade, anti-aircraft defense (air, submarine and surface means), mine warfare at sea, supporting ground forces with naval artillery, conducting amphibious operations, etc.
The model of missile defense/air defense operations in theaters is based on an assessment of the actions of the Patriot/THAAD, Aegis, and air-launched laser weapons. The missile threat and the functioning of an integrated theater missile defense system are simulated.
Modeling of control, communications, computer support, reconnaissance and surveillance (C4ISR) systems is based on a situational digital map of the situation, simulation of information flows on the battlefield, collection and aggregation of information about the situation with target recognition, setting tasks for detection means, including space-based ones, and etc.
The decision-making process is based on a knowledge base of tactical standards as well as the preferences of decision makers.
The system allows you to simulate the operation of electronic warfare equipment and evaluate the processes of restoring the control system after exposure to the enemy.
When modeling information operations, the direct impact on enemy communication, detection and information processing systems is simulated.
Currently, it is impossible to assess the consequences of dynamically introducing information viruses or distorting information in enemy computers or information flows, and there is also no possibility of uncovering misleading measures (planned to be implemented in subsequent versions).
Modeling the functioning of space forces and assets takes into account the planned modernization (prospective appearance) of forces and assets, processes of space control, simulation of counter-space operations and information warfare.
Logistics support is modeled taking into account autonomy, planning of transportation of forces and assets by air, rail, road, sea and pipeline transport, support from allies, etc.
Examples of tasks solved using JWARS in network-centric warfare include evaluating the effectiveness of:
Protection of critical facilities (US territory, bases, military groups in the theater of operations, allied forces and facilities, etc.);
Neutralization of weapons of mass destruction and their means of delivery;
Information systems protection;
Measures to counter the enemy through continuous observation, tracking, massive impact by high-precision air and ground means against critically important stationary and mobile targets;
New information technologies and innovative concepts for developing the architecture of a “unified” control system and a unified map of the operational situation, etc.
JWARS includes a production expert system with inference based on “if.., then.., otherwise...” decision rules. Updating the knowledge base (meanings of facts, rules) about the enemy is carried out as a result of the information reconnaissance process. Knowledge base
also contains information about your forces, the results of assessing the situation, including the enemy. It provides users with automatically generated solutions that can be modified interactively. The decision rules of the knowledge base are key to the dynamic functioning of the model. As a result of the rule triggering, each fact can be assigned one or more actions. Actions are executed when the value of a calculated fact becomes equal to a certain threshold and produces a change in the state of the database.
Triggering of the rules also automatically generates requests to the intelligence system, which issues notifications (responses) to these requests. The operation of the rules determines the dynamics of the model's behavior over time. The responses generated by the intelligence system are evaluated by the criterion of satisfaction (the degree of satisfaction of the request). In the case of a low satisfaction rate, the request is reformulated taking into account the interdependence between requests and the state of the operational situation.
When assessing the operational situation, a digital geographic map with a coordinate grid (Common Reference Grid) is used. For each cell of the coordinate grid corresponding to a land area, the value of the indicator is calculated, characterizing the degree of control of the situation of one’s own forces and the enemy, based on calculating the “power of influence” using a certain method. As a result, each cell is colored blue or red.
The model of the processes of detection and classification of objects (targets) is stochastic in nature, depending on the actions of enemy forces, visibility, the degree of electronic countermeasures, and the nature of the terrain. Based on the calculated probabilities, the number of detectable enemy forces and weapons from those actually present is determined, then the probabilistic process of recognition/classification of targets is modeled, as a result of which they are correlated, for example, either with a specific type of weapons and military equipment, or only with a certain class of samples. Then a final report of the detection tool’s operation is generated.
The process of association and correlation of the results of the work of various intelligence means in a single information space is as follows:
1. The results of the detection of each reconnaissance means are plotted on the situational map.
2. The positions of each of the previously detected objects are extrapolated in time to the time of receipt of new reports on the results of the work of reconnaissance means.
3. Based on the calculation of the location of the “center of mass” of previously discovered objects, probable candidates are selected for association with objects, information about which is contained in newly received reports on the results of the work of reconnaissance means.
4. The probabilistic value of the association of objects is calculated.
5. Based on the relative value of the association probability, it is determined whether the object is a newly discovered one from previously known ones or a new object discovered for the first time.
Nature of algorithms used in JWARS:
1. Probabilistic (stochastic) process (Monte Carlo) - calculations based on random number generators, discrete output quantities (modeling of detection processes, planning of air strikes on ground targets, missile defense/air defense in theaters, mine warfare at sea, anti-submarine warfare, confrontation between surface forces of fleets, etc.).
2. Deterministic calculations (analytical and based on probability theory formulas). It is possible to simulate the processes of using and protecting against weapons of mass destruction, maneuvering forces and means.
Properties of the JWARS model characteristic of network-centric military operations:
The ability to dynamically and interactively respond to ongoing events based on the perception of the situation by each side based on an analysis of the operational situation;
Creating a basis for decision-making using an analytical assessment of the current situation;
Implementation of a high degree of coordination/synchronization of the actions of the commander of the United Nations Front with the actions of subordinate commanders at all levels of leadership;
Integration of intelligence information for decision making;
Modeling the behavior of “key objects” (centers of gravity) - military and economic - in relation to the state of the enemy’s airspace;
Assessing the implementation of the ultimate goal of a military operation (end state), for example in the form of a change in the policy of the state leadership;
Description of the aggregate criteria for achieving victory (geographical - the absence of enemy units in a certain territory, the desired balance of forces - avoiding losses of one's own forces and allies, defeating the enemy within a certain time);
Determining the degree to which the objectives of a military operation have been achieved.
Software-wise, the JWARS system consists of three modules: functional, simulation and system, which are combined into a single complex. The functional module contains application software that allows you to simulate combat functionality. Special software of the simulation module creates a virtual image of the battle space. The system module ensures the functioning of the JWARS system hardware and creates human-machine data exchange interfaces, with the help of which input data is entered and simulation results are obtained.
Functional module. The main element of the JWARS system is the object
combat space - Battle Space Entity (BSE). Nominal level of detail: battalion for combined arms operations, squadron for air operations, ship for maritime operations, and reconnaissance platforms for reconnaissance and surveillance systems. Auxiliary objects of the combat space are infrastructure facilities (ports, airfields, etc.), control posts (headquarters, command posts, communication centers, etc.). Combat space objects are characterized by static (for example, the radius of destruction of strike weapons) and dynamic (in particular, location coordinates) properties. The data also includes information about the interactions of objects with each other and with the external environment.
The interaction of battlespace objects in the JWARS system is implemented using various algorithms, which vary depending on the nature of the activity being modeled, the functionality of the model with which the algorithm is associated, and the availability of data. All interactions between battlespace objects in JWARS are simulation events. The significance of individual events can range from relatively low to very high.
Simulation module. This module contains tools for simulating the necessary infrastructure, developed in an object-oriented manner, which ensures their modularity and, therefore, sufficient flexibility necessary to quickly make changes to the virtual battle space.
The JWARS system has stringent requirements for data storage and processing. Meeting these requirements requires a robust database management system. For these purposes, JWARS uses the ORACLE database management system (DBMS), which is used to store all information, including both input and output.
Like other simulation systems of the latest generation, JWARS necessarily supports HLA architecture standards.
System module. It includes the JWARS hardware that users use to perform simulations. The human-machine interface is used in the development of combat scenarios, reconnaissance of the battle space, implementation of combat command and control, as well as in analyzing the results.
Simulation of a wide range of military units in JWARS is ensured by the use of knowledge bases about event data, rules and cause-and-effect relationships, which together make it possible to analytically describe the position of friendly formations and enemy troops (forces), as well as external conditions. According to the developers, a relatively small set of cause-and-effect relationships makes it possible to simulate various military operations with a fairly high degree of realism without human intervention.
Earlier versions of the JWARS system made it possible to take into account factors such as the level of training of personnel and their moral and psychological state. As a result, there were opportunities to create units of different levels of combat effectiveness, with different personal qualities of commanders, such as a penchant for adventurism, concern about a poor solution to the assigned combat mission, etc. These characteristics provide a certain flexibility in creating a strategy for the behavior of certain units. In the latest versions of JWARS, a strict hierarchy of the command line for setting tasks was established, which made it possible, in general, to simulate a real assessment of the performance of tasks by subordinate units and to develop optimal options for their combat use. In other words, higher authorities set a combat mission and introduce restrictions to solve it.
The main goal of creating cause-and-effect relationships is to automatically reproduce the behavior of a unit based on the developing combat situation. It is possible to use the cause-and-effect data creation wizard to develop an unlimited number of new rules.
Because rules can be saved as data, it is easy to create rule sets without changing the JWARS code.
The simplest JWARS rules use rudimentary logical relationships (greater than, and, or, etc.), while more complex reasoning about whether a situation is favorable or not relies on more complex relationships (if, then, otherwise).
One of the trends in the development of this toolkit of the JWARS system will be the implementation in the near future of the possibility of constructing logical cause-and-effect rules based on the mathematical apparatus of fuzzy logic.
To facilitate the user's application of fuzzy rules, a system of automated help and an intuitive graphical interface will be implemented.
Units in the JWARS system have a variety of capabilities and can perform different actions or tasks at the same time, as long as they do not conflict with each other (for example, stay in place and move). The unit's actions may be changed depending on the completeness of information about the situation. For example, when faced with superior enemy forces, a unit with incomplete information regarding the location of other friendly allied forces may retreat until the situation becomes more certain. The more uncertain the situation, the sooner the retreat will begin. Once the situation is determined, special actions can be taken to suit the moment. The unit must use all the resources at its disposal in order to solve the assigned tasks without violating restrictions, for example, regarding the number of losses of personnel and equipment.
In earlier versions of JWARS, which did not have a tactical-level cause-and-effect system, there were instances where during the simulation, combat units moved toward their targets by returning fire instead of engaging in combat. There were also cases of units engaging in combat inappropriately. The knowledge base of cause-and-effect relationships made it possible to improve the ability to assess the situation and make changes to the options for the combat use of units. As shown in the figure below, the unit attacks the enemy, closes with them, destroys them or forces them to retreat, and then resumes the original mission. Meanwhile, support units, both friendly and enemy, assess the situation as dangerous and try to stay out of the firing range.
JWARS rules can be easily associated with specific types of departments. This allows users to form new units and automatically assign them appropriate sets of rules and actions based on different combinations of characteristics. Any unit created as a combat unit (armored, infantry, etc.) can inherit these rules. However, some rules for small units (deep reconnaissance groups, special forces groups) may be more important in relation to the general combat rules.
To ensure the actions of non-combat units, appropriate rules are developed, which, for example, force them to change course in order to avoid collisions with the enemy. Combat and non-combat units, obeying the order of a general superior to move to a specific location, determine their route based on existing rules. In this regard, significant differences in their routes are possible.
The practice of using JWARS shows that sets of fuzzy rules are a good tool for making complex decisions, since they not only provide the ability to choose among predefined action options, but also allow the generation of new ones. However, this system still primarily uses standard rather than fuzzy rules due to the completeness of the standard rule sets and their ease of use in structured decision making. Most experts believe that standard rules are much easier to formulate. However, future versions of JWARS will improve the editing and automated testing of fuzzy rules to make them easier to work with.
One of the key aspects of the activities of military units is joint actions. Since one of the main functions of the system is to evaluate the effectiveness of the actions of various structures, joint actions must be a very flexible component of the model. For example, resources for JWARS units may come from multiple sources, some of which are preferable in certain contexts, but none of which meet the minimum requirements. Understanding this trade-off will be a major challenge in applying knowledge bases to areas where limited resources are shared. Units in the JWARS system do not agree on joint actions or form temporary coalitions, but request additional resources and use supplies based on an assessment of the situation. Thus, a unit participating in combat operations can request additional fire support and receive it from one or more sources depending on its priorities. At the next request, another unit or type of weapon may act as support, but in any case, support will be provided until all resources are exhausted.
In general, it should be noted that the development of modeling and simulation systems in the United States is considered as one of the main factors in ensuring the efficiency of construction and use of aircraft. The enormous potential accumulated in this area is already assessed as significantly ahead of the capabilities of other countries in the world in this area. In the future, further global integration of models and the introduction of virtual reality systems (artificial multidimensional combat space) based on telecommunication networks are expected, designed to provide users with access to both operational and physical simulated environments, standardized models and databases, as well as various kinds of scenarios. Prospective systems for modeling combat operations will simulate the use of armed forces on any continent, at sea, in the air and outer space, the entire range of their involvement (including peacekeeping operations, the fight against terrorism, etc.). Future systems will be able to simulate with a high degree of accuracy actions against the background of an artificially created combat situation, reproducing the features of any theater of operations. The enemy will be both fully and partially computerized “analogs” of real military formations.
According to the degree of human involvement, foreign experts clearly divide all modeling and simulation tools into full-scale, virtual and constructive. Constructive means involve the use of virtual troops (forces) in a virtual combat space.
HLA architecture is understood as the structure of a simulation system at the level of interconnections of individual components, as well as standards, rules and interface specifications that determine the interaction of models during development, modification and operation.
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“Military Thought” No. 5.2004.
MILITARY THEORY AND PRACTICE
Colonel A.A. EGOROV, Candidate of Military Sciences
In MODELING, as in any creative activity, various concepts for constructing mathematical models are possible, including those that are characterized by innovative ideas that involve a deviation from generally accepted principles and rules of modeling. This is, for example, an attempt to formalize the mental and psychological activity of military leaders and military personnel of the warring parties, the use of situational modeling, etc. Today, a large number of mathematical models have been developed, different in structure and content, but they are all designed to solve practically the same problems.
Despite the multiplicity of views on modeling methods, mathematical models still have some similarities that allow them to be combined into separate classes. The existing classification of mathematical models of combat actions (operations) of an air force unit takes into account the following characteristics: target orientation; way of describing functional connections; the nature of dependencies in the objective function and restrictions; time factor; a method of taking into account random factors. Although this classification is conditional and relative, it still allows us to bring our knowledge about modeling into a specific system, compare models, and also develop promising directions for their development.
However, this classification of models of combat actions (operations) does not give a complete picture of the methods for constructing models intended to find the best options for conducting combat actions (operations) of an air force unit, the hierarchical structure of such models, and the completeness of taking into account various “types” and “kinds” of them. » uncertainties that have a dominant influence on the course and outcome of simulated combat actions (operations). To verify this, it is enough to analyze the existing classification of models of combat operations (operations) of the Air Force association. According to it, depending on the target orientation, mathematical models of combat actions (operations) are usually divided into “evaluative” and “optimization”.
In evaluative (descriptive) models, the elements of the intention (decision, plan, option) of the parties' proposed actions are given, that is, they are part of the initial information. The result of the simulation is the calculated results of the actions of the parties in hostilities (operations). Such models are most often called models for assessing the effectiveness of combat operations (operations). For them, developing rational methods of using forces and means is not the main task.
In optimization (optimizing, normative) models, the ultimate goal is to determine the optimal methods of conducting combat operations (operations). These models are based on mathematical optimization methods. Compared to evaluation models, optimization models are of the greatest interest for planning combat operations (operations), since they allow not only to quantitatively assess the effectiveness of options for conducting combat operations (operations), but also to search for the most effective options for a specific situation.
Since today there is no single optimization method that allows taking into account the entire range of cause-and-effect relationships of combat actions (operations) of the Air Force, existing models for finding the best options for using troops (forces) are structurally a combination of various mathematical optimization methods. The peculiarity of constructing such combined models is that the task of modeling combat operations is divided into a number of subtasks, each of which is solved by a long-proven classical optimization method. For example, the subtasks of distributing air strike weapons to targets and the subtasks of distributing air defense weapons to air targets are solved using nonlinear programming methods, and the subtasks of constructing flight routes to target targets are solved using dynamic programming.
However, the combination of optimization methods in the model does not allow achieving the main goal of modeling combat actions (operations) to determine the best way to use troops (forces), since such an approach does not make it possible to fully take into account the deep interconnection of the processes characterizing the course of armed confrontation. This is due to the fact that these subtasks have different solution conditions. For example, the subtask of distributing strike aircraft to ground targets is solved separately from the subtask of determining the optimal (rational) method of breaking through air defenses. At the same time, these are interrelated issues, since the degree of penetration of the enemy’s air defense determines the amount of losses during a combat mission of our strike aircraft, which is precisely what is to be distributed among the targets of the air strike.
To ensure comprehensive optimization of the actions of troops (forces) in each episode of simulated combat actions (operations), a new method for constructing models - the suboptimization method - is proposed. It involves searching for rational ways of conducting combat operations (from top to bottom) sequentially at each level of control, but within the framework of the overall plan of combat actions (operations). The undeniable advantage of suboptimization is that at each level of control the factors and conditions of combat operations of formations and units are identified in more detail and the most reasonable methods of their actions are selected.
Thus, taking into account the need of the commanders and staffs of Air Force formations to effectively ensure the search for rational options for conducting combat operations (operations), it is necessary to introduce a new classification of optimization models of combat actions (operations) of the Air Force formation, which provides for the division of models into combined and suboptimization. This can help users significantly expand their understanding of the features of the construction and functioning of models designed to find rational methods of conducting combat operations (operations).
The hierarchy of decision-making for combat actions (operations) cannot but be reflected in the construction of mathematical models of combat actions (operations) of the Air Force unit, since the paradigm for constructing models is the maximum reflection of the simulated reality.
However, the developers of existing operational-level models understand the modeling paradigm one-sidedly, namely: models are built only by the method of detailed reproduction of air and anti-aircraft battles that constitute the main content of combat operations (operations). At the same time, due attention is not paid to the detailed reproduction of the hierarchical essence of decision-making at all levels of command, which provides commanders of formations and units with the opportunity to exercise reasonable initiative, but within the framework of the overall plan of combat actions (operations) of the association.
Models of direct reproduction of only air and anti-aircraft battles can be classified as single-level models. But since tasks at the operational level are also solved within the framework of the tactical level (“on the field” of the tactical level), the mathematical model becomes cumbersome and inconvenient for practical use. The use of such models is associated, firstly, with the need to prepare a large amount of initial data, secondly, with a decrease in the efficiency of direct modeling of combat actions (operations) and, thirdly, with the difficulty of perceiving the obtained modeling results.
The structure of multi-level mathematical models of combat actions (operations) is an integral system of functionally interconnected submodels (aggregates) of various levels, which are interconnected not only by horizontal relationships among themselves, but also by relationships of subordination. The compositional approach in multi-level models can be considered as one of the promising ways to improve them while maintaining the required degree of detail in the modeling of combat actions (operations). A system of submodels at various levels of control creates favorable conditions for modeling combat actions (operations) using parallel or combined methods of planning combat operations. The efficiency of planning is increased mainly due to submodels at the tactical level. Preparation of initial data, modeling and interpretation of its results on sub-models of the tactical level are carried out in parallel by the corresponding commanders and their staffs.
The proposed approach to the construction of mathematical models of combat actions (operations) of the Air Force association, which involves the use of a method of detailed reproduction of the hierarchical essence of decision-making for combat actions (operations), made it possible to introduce another criterion for classifying mathematical models according to the hierarchical structure. According to this criterion, mathematical models can be classified into single-level and multi-level.
In the existing classification of mathematical models of combat actions (operations), an important place is occupied by classification according to the method of describing functional connections between parameters (processes of functioning of system elements). In accordance with this feature, mathematical models are divided into analytical and simulation.
In analytical models, the processes of functioning of system elements are described in the form of certain functional relationships or logical conditions. The most complete study of the process can be carried out if explicit dependencies are known that connect the output characteristics with the initial conditions and input variables of the system. However, such dependencies can be obtained only for relatively simple models or under very strict restrictions imposed on the modeling conditions, which is unacceptable for modeling combat actions (operations) of an air force unit.
Depending on the type of analytical dependencies used in them (objective function and constraints), analytical models are usually classified into linear and nonlinear. If the objective function and constraints are linear, then the model is called linear. Otherwise the model is nonlinear. For example, models based on the linear programming method are linear, but in models based on the maximum element or dynamic programming methods, the objective function and (or) constraints are nonlinear.
In simulation models, elementary phenomena (battles, air strikes, special combat flights) that constitute the main content of combat actions (operations) are imitated (copied) while maintaining their logical structure and sequence of occurrence (in time), which makes it possible to evaluate their characteristics at certain points in time . Simulation models make it possible to quite simply take into account factors such as the presence of discrete and continuous elements, nonlinear characteristics of system elements, numerous random influences, etc. Currently, simulation modeling is the most effective and often the only available method for studying complex systems such as combat operations (operations) Air Force associations.
Depending on the consideration of the time factor, models of combat actions (operations) are divided into static, dynamic, continuous and discrete.
Static models are used to describe combat actions (operations) at any point in time. They reflect a certain “time slice” of combat operations (operations). Therefore, static models are used to study the most important stages of combat operations (operations). As a rule, this is the initial stage, the outcome of which largely determines the further course of events and the final result of the operation.
Dynamic models describe combat actions (operations) in development. This makes it possible to identify trends in the development of combat operations (operations), factors and relationships that, at first glance, do not have a significant impact on the simulated process, but can become an important subject of consideration. The trend in the development of dynamic models of combat operations (operations) is clearly aimed at strengthening their role in the study of methods of using troops (forces) of the parties. Thanks to the ability to reflect the continuity between individual episodes of combat operations (operations), dynamic models have found worthy application for solving problems of long-term planning and forecasting the use of troops (forces).
Mathematical models of combat actions (operations) with continuous simulation time are characterized by the fact that their variables and output parameters change continuously, without jumps, and consistently take on all possible real values over the entire time interval. Continuous models use interpolation to find intermediate values. Since it involves finding intermediate values of a function, the model should be based on an analytical method that ensures the functional dependence of the initial and final values. Analytical methods are the least suitable for describing the entire set of factors in combat operations (operations) of an air force association, therefore continuous models have not found wide application for finding ways to use troops (forces).
Discrete models have become quite widespread in the modeling of combat actions (operations) of Air Force formations. The main advantage of the latter is that to construct them it is not necessary to have an analytical relationship between input and output quantities and you can use the simulation modeling method.
In discrete models, all processes (input and internal) are distinguished by an abrupt, pronounced change in a finite number of states: input, output and internal. Moving forward in a discrete model of combat actions (operations) sequentially from episode to episode with a given time step of modeling, the commander and his staff receive a comprehensive, systemic view of the processes occurring during combat actions (operations). The size of the modeling step varies and can be selected based on the required depth of modeling of individual episodes. If it is necessary to study more deeply a particular moment of the operation, the step size is reduced.
The development and outcome of combat operations (operations) of an air force association is influenced by a large number of factors, which are mainly of a probabilistic nature. Depending on the method of taking into account random factors, mathematical models of combat actions (operations) are usually classified into deterministic, stochastic (probabilistic) and combined.
However, this classification requires important clarification regarding stochastic (probabilistic) mathematical models of combat actions (operations). The name of the class “stochastic (probabilistic) models” does not give a complete idea of how other “types” and “kinds” of uncertainties are taken into account in models. In order to clarify the classification of mathematical models of combat actions (operations) according to the method of taking into account random factors, we will consider in detail the components of this class.
A characteristic feature of deterministic models of combat actions (operations) is that for a given set of input values of the model, a single result is always obtained. Each method of using troops (forces) chosen by the commander of the Air Force formation leads to strictly defined consequences, since during the modeling random, previously unforeseen impacts are neglected.
Deterministic models can be considered as a conscious simplification of reality, which is actually uncertain. Until the time when powerful computing tools began to be used at headquarters, deterministic models were the main tool for assessing the effectiveness of combat operations (operations). All stochastic uncertainty was “hidden” in the initial data, in particular in the probabilities of hitting air targets and ground objects, as a result of which the probabilistic problem became deterministic and was solved by conventional mathematical methods.
In order not to complicate the accounting of uncertainties caused by weakly predictable actions of the enemy, the most probable (as a rule, typical), in the opinion of military experts, options for the enemy to use its troops (forces) were studied in deterministic models. Therefore, deterministic models can be considered only one of the stages in the scientific study of armed confrontation.
The most promising class of models are non-deterministic models, since, compared to deterministic ones, they allow one to study a larger number of possible options for enemy actions during the conduct of combat operations (operations) of an air force unit. It must be emphasized that these are non-deterministic, and not stochastic (probabilistic) models, as is customary in the practice of modeling combat actions (operations). This clarification is very important. The previous classification of models of combat actions (operations), in fact, ignores the presence of another type of non-stochastic (real) uncertainties. This type of uncertainty includes uncertainty of nature, that is, the external environment, uncertainty of goals (the degree to which the desired result corresponds to real capabilities), and uncertainty of the enemy’s actions.
Nonstochastic uncertainties of armed confrontation, especially uncertainties of enemy actions, play almost a decisive role in modeling combat actions (operations). The collision of warring parties pursuing opposing goals has a significant impact on the scenario for the development of military operations (operations). For each such scenario, the commander and his staff choose a rational method of using their troops (forces). To some extent, non-stochastic uncertainty is primary in relation to another type of stochastic uncertainty, since the parties can choose such options of action that reduce the number of random elementary events.
Non-deterministic models more realistically reflect the complex influence of non-stochastic and stochastic uncertainties on the course and outcome of combat operations (operations). The impact of these uncertainties in non-deterministic models is assessed taking into account the most significant factors causing the manifestation of these uncertainties. Thus, to take into account non-stochastic uncertainty, it is envisaged that the enemy is practically unlimited in the choice of options for using his troops (forces). To study stochastic uncertainties, random processes associated with the destruction (detection, electronic suppression) of air targets and ground objects are reproduced taking into account the design errors of the weapons (detection), the range to the target and its angle, the possibility of an air target performing an anti-missile maneuver, camouflage of ground objects damage, electromagnetic environment, etc.
According to the method of taking into account random factors, in addition to deterministic and non-deterministic models, a class of combined models should be distinguished. They use techniques for taking into account uncertainties that are characteristic of both deterministic and non-deterministic models. Among the combined models, one can single out those in which the influence of stochastic uncertainty on the result of modeling combat actions (operations) is most deeply studied, or, on the contrary, weakly predictable actions of the enemy are assessed, and the probabilistic nature of elementary events of destruction (detection) of air targets and ground objects is taken into account in the initial data in the corresponding values of the initial probabilities.
From the point of view of taking into account non-stochastic uncertainties, mathematical models can be classified into models based on game theory methods and situational (war games). Their fundamental difference lies in one important limitation, namely the assumption in game theory models of the complete (“ideal”) intelligence of the opponent. Relying on an intelligent opponent is only one of the possible positions in a conflict, but in game theory it is the basis. In a real conflict, the choice of a rational method of using troops (forces) often consists in guessing the enemy’s weaknesses and taking advantage of them in a timely manner.
This is why situational models (war games) are becoming most popular. As in real combat operations (operations), situational models provide that the human factor can interfere with their course at any time. Moreover, the players on both sides are practically unlimited in choosing a strategy for their behavior. Each of them, choosing his next move, can, depending on the current situation and in response to the steps taken by his opponent, make one or another decision. He then runs a mathematical model that shows how the situation is expected to change in response to this decision and what consequences it will lead to over time. The consequences may be the possible number of losses of the parties, the number of air defense systems, strike weapons, control and communications posts suppressed by jammers, etc. The next “current decision” is made taking into account the real new situation. As a result, a rational solution is selected after repeating this procedure many times.
An important feature of game and situational models is the desire to deeply consider all possible types of actions and reactions, to identify and study possible options for the use of troops (forces) under the influence of the enemy.
Depending on the number of parties involved in the simulation of combat actions (operations), non-stochastic models can be divided into bilateral (“paired”) and multilateral (“multiple”), there are many combinations and types of which, including models associated with the participation of a large number of players and many intermediaries. Participants in “multiple” models can be not only direct opponents, but also representatives of troops (forces) interacting with the Air Force association, intermediaries, etc. Independent military experts who have the ability to intervene, if necessary, in the course of modeling combat actions (operations) can act as intermediaries.
From the point of view of taking into account stochastic (probabilistic) uncertainty, mathematical models of combat actions (operations) can be divided into probabilistic and statistical. The motivation for this classification is the difference between the problems of mathematical statistics and probability theory.
The problems of mathematical statistics are to a certain extent the inverse of the problems of probability theory (despite the fact that it is based on the concepts and methods of probability theory). In probability theory, the probabilistic characteristics of random events of destruction (detection, electronic suppression) of air targets and ground objects are considered given. Based on the given characteristics, the effectiveness of combat actions (operations) is calculated, for example: the mathematical expectation of the number of objects saved, the mathematical expectation of the number of air targets hit, etc.
In mathematical statistics, it is assumed that the probabilistic model is not specified (or not fully specified), and as a result of a machine experiment, the realizations of random events became known. Based on these data, mathematical statistics selects a suitable probabilistic model to draw conclusions about the phenomena under consideration associated with the destruction (detection, suppression) of air targets and ground objects.
In the early stages of mathematical modeling, including modeling of combat actions (operations), the probabilistic approach was the most popular method of taking into account stochastic uncertainty. This is due to the fact that the volume of calculations of statistical methods compared to probabilistic methods is excessively large. To obtain reasonable modeling results using statistical methods, high-speed computers are required.
With the development of computer technology, statistical methods are increasingly used to take into account the stochastic uncertainties of combat operations (operations). The statistics of a computational experiment on the destruction (detection) of air targets and ground objects, obtained during the simulation of combat actions (operations), contains information about the conditions of the experiment: design errors of the weapons (detection); range to the target and its angle; the ability for an air target to perform an anti-missile maneuver; camouflage of ground targets; electromagnetic environment. In probabilistic models, the probabilistic characteristics of random phenomena of destruction (detection, suppression) of air targets and ground objects must be specified in advance, which is difficult, since it is impossible to accurately predict the environmental conditions in which the destruction (detection) of air targets and ground objects will be carried out.
Thus, we can give a refined classification of mathematical models of combat actions (operations) of an Air Force formation**, which can be carried out according to the following criteria (Table):
target orientation; the method of constructing optimization models; hierarchical structure; method of describing functional connections; the nature of dependencies in the objective function and restrictions; taking into account the time factor; method of taking into account random factors; taking into account non-stochastic uncertainties; the number of parties involved in the modeling; taking into account stochastic uncertainties. In the table, new and refined classes of mathematical models are highlighted in bold.
The main focus of the refined classification is to establish clear boundaries between models of combat actions (operations), and most importantly, to identify trends in the development of mathematical modeling of such complex systems as the models of combat actions (operations) of the Air Force. As a result of the classification, it was established that the main trends in mathematical modeling of combat actions (operations) are: firstly, the development of sub-optimized mathematical models designed to find optimal options for conducting combat actions (operations) of an air force association; secondly, disaggregation of the large-scale task of modeling combat actions (operations) through the use of a method of detailed reproduction of the hierarchical essence of decision-making for combat actions (operations); thirdly, the creation of a class of models that correctly take into account the impact of both stochastic uncertainties associated with the destruction (detection) of air targets and ground objects, and non-stochastic ones caused by difficult to predict enemy actions.
Mathematical modeling and assessment of the effectiveness of combat operations of the Air Defense Forces. Tver: VA PVO, 1995. P. 105; Military thought. 1989. No. 2. P. 38; Military thought. 1987. No. 7. P. 34.
Optimization methods include analytical methods (Lagrange method, Lanchester equations), iterative (methods of linear, nonlinear, dynamic programming), non-iterative (methods of random search, multivariate analysis), as well as methods of sequential optimization (situational method, methods of coordinate search and fastest descent).
Military thought. 2003. No. 10. P. 24.
Military thought. 2003. No. 10. P. 23-24.
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