Combat Simulation Techniques for Analysis of Future Battlefield Digitisation

Transcription

1 Combat Simulation Techniques for Analysis of Future Battlefield Digitisation Duncan Tailby; Rebecca Cause; Colin Stanford Land Operations Division, DSTO Abstract. The Australian Army's goal is to be a concept-led and capability based force. Therefore DSTO is developing detailed simulations that support the analysis of future force concepts. This paper describes an analytical modelling technique for representing future forces in the Combined Arms and Support Task Force Evaluation Model (CASTFOREM). One application of the model is a quantification of the impact of battlefield digitisation on a future force's ability to achieve its mission. The results from such a study have been used to guide future force concepts and capability development. In this application it is important to consider how computer generated forces interacted with a simulated information architecture to enable the acquisition and exchange of information to support decision making within a specific mission context. Of particular relevance is the fidelity of the human behaviour modelling and the supporting decision architecture such that the fidelity of the modelling matches the fidelity of the analysis required. The requirement for algorithm, data and overall performance validation is addressed, as are limitations and assumptions inherent in using such a simulation in this particular application. Methods of improving the simulation to enhance its utility are also covered. 1 INTRODUCTION DSTO has been a partner in the Army Experimental Framework (AEF), which sought to embody the Army s concept-led approach to capability-based modernisation program. A focus of the AEF has been the examination of the war fighting concepts relevant to Manoeuvre Operations in the Littoral Environment (MOLE). 1.1 Headline Experiment The investigation of the MOLE concepts led to the development of the Headline Experiment (HE99), a series of seminars and interactive wargames examining the application of the concepts to design a future Army brigade. In order to conduct post experiment analysis of HE99 results, the Combined Arms and Support Task Force Evaluation Model (CASTFOREM) simulation was employed as the final analytical tool of the experimental program. CASTFOREM is a systemic, high-resolution, event driven, stochastic simulation of the combined arms battle. It allows the combat effectiveness of a combined-arms team to be quantified and the impact of changes in doctrine, TTP, organisation and equipments, to be assessed. CASTFOREM is used by the US Army s TRADOC Analysis Organisation as its primary brigade and below entity level analytical simulation. Its high degree of acceptance in the US Department of Defense affords the model, and its algorithms, a degree of validation that proved attractive to Land Operations Division (LOD) DSTO when it was acquired in 1997 as its main analytical task force level simulation. In the CASTFOREM phase of the analysis, future forces were represented in the model with the primary aim of investigating the high level statement that: Situational awareness promotes mission success through superior force utilisation The aim of the experimentation was to inform decisions on the types of generic capabilities required by a future land force to achieve success within a specific mission context. The investigation focused on force concepts at the generic capability level as opposed to specific platform options. The remainder of this paper will demonstrate the simulation techniques used to quantify the impact of varying battlefield information in a combat model. Specifically the analysis investigated the capability gained by a force as a result of battlefield digitisation. The requirement for algorithm, data and overall performance validation is addressed, as are limitations and assumptions inherent in using such a simulation in this particular application. Methods of improving the simulation to enhance its utility are also covered. 1.2 Battlefield digitisation Network Enabled Warfare (NEW) for the purposes of this paper is defined as: an approach to the conduct of warfare that derives its power from the effective linking or networking of the war fighting enterprise. It is characterised by the ability of geographically dispersed forces (consisting of entities) to create a high

2 level of shared battlespace awareness that can be exploited via selfsynchronisation and other networkcentric operations to achieve commanders' intent. (Doctrine Wing CATDC 1999) An enabler for effective NEW is battlefield digitisation. Battlefield digitisation specifically covers the application of technologies to acquire, exchange and employ timely digital information throughout the battlespace, tailored to the needs of each commander, shooter and supporter (Young and Dzierzanowski 1999). Battlefield digitisation facilitates a higher level of situational awareness between elements on the battlefield. It was hypothesized during the HE99 that by applying NEW concepts a force would be more effective. Specifically the higher the level of situational awareness, the more effective the force. Command and Control Human Behaviour Physical Effects attached forces. This includes a range of personnel, equipment, communications, facilities and procedural systems which are employed by a commander to plan, direct and control forces and operations in the accomplishment of a mission (Tailby et al in preparation). From a modelling viewpoint, the processes of C2 overlap both the physical and human behaviour components of simulations as represented in Figure 1. The C2 processes within a system can be viewed as a causal loop between human behaviour aspects and physical effects (Figure 2). Battlefield digitisation is, in the main, a physical effect in that it is the process of improving the application of technologies to acquire, exchange and employ digital information in a timely fashion. Assumed in this process is that the human component involved in the application of the information of the system, shown in Figure 2, is able to undergo a similar performance improvement in terms of doctrine and organisation matching the performance increase of the physical systems. In addition it is generally assumed that the human system can process more information without degradation of performance. Hence the modelling of battlefield digitisation, and its resultant effects in terms of a combat outcome, can be attributed to changes in physical effects as opposed to human behaviour changes. It has already been noted that the two processes are linked. The degree to which an enhancement in physical effects (i.e. communications architecture) impacts human behaviour is discussed in Section 5 and Section 6. Figure 1: Elements of combat simulations 1.3 Current Simulation Tools Simulations of military environments model two broad aspects of combat: the physical actions and effects generated by military equipment and the human behaviour components of monitoring, planning and decision making. In general the current suite of military simulations are adequate in one or the other of these aspects but few are able to combine good modelling of both aspects into one simulation (Bowley and Lavaszy 1999). Emerging technologies, such as intelligent agents, are being incorporated into simulations that will combine high fidelity modelling of both aspects of combat. At this stage none of these simulations operate, or have been validated, for use at the scale required for conducting closed loop analytical simulation of the Task Force level concepts investigated in the HE99. 2 MILITARY COMMAND AND CONTROL Within a military system the process linking the aspects of physical effects and human behaviour components is known generally as command and control (C2). C2 includes all systems and personnel involved in exercising authority and direction over assigned or Human Behaviour - monitoring - planning - decision making C2 Physical actions and effects Figure 2. C2 integration process 3 CASTFOREM TACTICS REPRESENTATION The generic military decision cycle shown in Figure 2 can be further refined and viewed as a process of integration of battlefield resources. The commander makes a tactical allocation of a set of resources (decision) and then those resources are physically

3 integrated (action) to achieve some battlefield objective. This cycle is echelon invariant and applies to an individual entity or a complete military organization. Such a representation is shown in Figure 3. Tactically allocate resources C2 Physically integrate resources Figure 3. Generic military decision cycle The design of CASTFOREM is such that it implements the military decision cycle as an expert system. The major components of the expert system are the knowledge base, inference engine and entity situational profile, as seen in Figure 4. The flexibility and high fidelity representation of battlefield tactics in CASTFOREM is as a result of the design of the knowledge base, formally represented as a series of decision tables (DT). The DT model subjective human decision making through satisfying conditions or questions that result in an appropriate action. Decision tables operate on individual units of resolution. Logically, DT are a series of IF-THEN-ELSE statements arranged as sets of complete tactics within the context of the scenario. The situational profile describes a unit s weapon status, known enemy units and current location, amongst other details. The inference engine is generic code for processing an entity s situational profile and knowledge base to generate appropriate physical actions. Decision tables are a prescriptive model of behaviour. Production rule systems, as CASTFOREM is, use bottom-up reasoning that lack the causal reasoning (topdown) that humans perform in goal orientated behaviour. The three main criticisms of rule based systems are: they are restricted to simple forward chaining, event to situation they do not robustly take into account uncertainty in events, situations, and the rules linking them; and they do not make use of memory, but simply reflect the current instantaneous state (Pew and Mavor 1998). To a large extent the problems associated with using DT to represent decision making can be overcome by first exploring the scenario in a constructive wargame. This reduces the need for CASTFOREM to generate plausible behaviour to new or novel situations as the full range of reactions have already been explored in the wargame. However this is assuming the decisions made in the wargame are of a high quality. DT, despite their limitations, are a flexible way to represent complex tactics at various levels of fidelity. They can be concentrated to focus on the actions of one unit in a high degree of detail. Alternatively, they can be designed to control the detailed movements and actions of a large force simultaneously. The fundamental C2 cycle can be viewed as a process where the inference engine and knowledge base perform qualitative assessment of the situation to produce approximate future actions. The more complete the knowledge base the wider the number of actions and the stricter the criteria used for selecting a particular action, hence the more realistic the response. Once the future action(s) have been determined a quantitative calculation is performed where the actual physics of the process is known. The modelling of the military C2 cycle becomes a causal loop of qualitative assessment and prediction followed by quantitative assessment (Mackey 1990). Knowledge Base Inference Engine Revised Situational Profile C2 Communication Move Search Engineer Refuel Rearm Figure 4. CASTFOREM design The outcome of parameterising the knowledge base in the form of DT is that the simulation is not constrained to only varying physical parameters, but instead can vary the decision making process in a controlled fashion. It is this ability that makes CASTFOREM suited to studying the impact of digitisation on C2 from two perspectives. The first is from the physical perspective of enhancing communication and related technologies, as will be outlined in the subsequent sections of this paper. The second aspect is from the human perspective by varying the knowledge base,

4 which in effect is testing information doctrine. CASTFOREM combines the simulation of hard and soft systems to quantify the impact of digitisation in terms of a combat outcome. 4 MODELLING DIGITISATION CASTFOREM allows a network-enabled command and control approach to be modelled via a global information service which represents a shared awareness built up of information acquired from many different sources. Both voice and data communications are modelled over user defined networks with terrain effects such as path loss and transmit times incorporated. Within CASTFOREM, each organisational entity possesses a singular intelligence system which is updated by the acquisition of information either via the communication network or directly (detecting a target, encountering an obstacle, receiving fire etc). Simulating perfect and instantaneous exchange of information among organisational entities can represent perfect friendly knowledge. Delays and failures in the exchange of information over a communication network will cause each entity s intelligence system to perceive incomplete battlefield knowledge rather than perfect knowledge. A snapshot in time of an entity shows only the knowledge possessed by that entity at that given time, and the planned course of action which is planned based upon current knowledge or commands from outside sources. An entity can act autonomously or, providing it has communications, it may be ordered as a subordinate of a higher echelon unit. 4.1 Modelling Information The definition of NEW states that it derives its power from the effective linking or networking of the warfighting enterprise. Digitisation of the battlefield, the enabler of NEW, improves information exchange between entities. The result is that entities achieve information superiority. The Australian Army s definition of situational awareness (SA) is: the ability to interpret facts, information gaps and uncertainty in the battlespace in order to assist decision making. SA systems are the means by which a decisionmaker is provided with such information. (Australian Army 1999) SA involves three distinct components or levels: perception of critical factors in the environment (Level 1); understanding what those factors mean (Level 2); and prediction of the future status of operationally relevant factors, at least into the near future (Level 3). CASTFOREM is able to model Level 1 SA through its information exchange architecture and replicate Level 2 and 3 through the DT modelling of actions generated through constructive wargaming. In order to create a high level of shared battlespace awareness (Level 1 SA), a high level of factual information available to the force is implied. For this level of shared battlespace awareness to be exploited the information available to the force must be both relevant and timely. To investigate the impact of SA on the force s effectiveness in the investigations following the HE99, the timeliness and amount of information received by the commander was varied. The friendly force was assumed to maintain its relative information superiority by having a robust all-informed communications architecture. The CASTFOREM modelling focussed on how varying the completeness of threat position information impacted on the force s effectiveness and tempo of operations as indicated by Measures of Force Effectiveness (MOFE) and Measures of Effectiveness (MOE). The completeness of the information was varied by changing the time it took to acquire and process Enemy targets (acquisition processing delay time). The end result of this degradation of the sensoractor link is that the commander received less information. Increasing the acquisition processing delay reduced the completeness of the information since fewer targets were acquired, tracked and reported in a given time period. In real terms this had the equivalent effect of manipulating the intelligence processing in the battlefield. The transmission latency of the information remained relatively constant, as was the completeness of friendly position information. Figure 5 shows the four levels of information modelled and the impact of the acquisition processing delay time on the percentage completeness of information. The four levels of information are as follows. Ideal: no processing time (+ network latency + commander s decision cycle 1 ) High: minute processing time (+ network latency + commander s decision cycle) Medium: 1-2 minute processing time (+ network latency + commander s decision cycle) Low: 2-3 minute processing time (+ network latency + commander s decision cycle) The level of information being received by a commander is measured in terms of the percentage information collected by Unmanned Arial Vehicles (UAVs), and the total information collected by all assets. Since UAV surveillance systems initiated the 1 A commander s decision cycle was modelled to simulate a commander s attention not always being on the command support tools as well as the time to make a decision. This was randomly sampled from a uniform distribution.

5 major decision points in the friendly plan, modelling of its information acquisition has significant implications on the forces overall effectiveness. Percentage Threat Information Processing UAV % Total % Figure 5: Percentage threat information passed by surveillance assets based on processing delay 4.2 Communications There were two types of communication networks modelled, termed analogue and digital. The analogue network modelled current capabilities and architecture with C2 information transmitted by simulated voice messages over combat net radio. Limited data transmission capability and no voice contention were assumed. To achieve a high degree of fidelity, the analogue network models the talk time for each voice message. Figure 6 shows data collected during a digitisation field experiment on the situational report transmission distribution (Young and Dzierzanowski 1999). The data was curve fitted to a Log Normal distribution that was then sampled during the simulation to model the time it takes to transmit a situation report. As a result of the explicit modelling of talk time and the relaying of messages between the echelon networks a more representative model of a current battlefield communication system was achieved. The impact of such modelling on the results is the delay between decisions being made and the reaction of the manoeuvre units. In contrast, the digital network represented a future architecture, with a capability to implement higher levels of SA and NEW concepts. The architecture was based on a broadcast network as opposed to point to point communications. The future digital network has a much flatter structure than the analogue network. It provided the mechanisms for sharing friendly and enemy position information in an automated fashion. Number of Messages Seconds Figure 6: Analogue talk time distribution (Young and Dzierzanowski 1999) Overall the digital network, with its flatter structure and high capacity, low latency architecture, results in a more timely delivery of C2 information. This is due to a reduced number of transmits for a message to travel from the lower echelons to the upper echelons. The digital network also has delays associated with accessing the network but these are less than those of the analogue network due to the reduced transmission times of digital data. In this study, all transmissions across the communication networks were assumed to reach their destination, however the capability exists to degrade the network performance by modelling message failure and retransmission, jamming or terrain effects. The analogue network was used for C2 that involved passing orders point to point between echelons that resulted in the manoeuvre of the forces. This analogue network was implemented for both Red and Blue. The digital network was used for broadcasting SA information in the form of automated position updates known as Blue Situational Awareness (BSA), for knowledge of friendly forces, and Blue Situational Awareness of Red (BSAR), for knowledge of the Enemy locations. BSA and BSAR knowledge databases are internal to CASTFOREM and record an individual's perceived location of friendly and enemy platforms. Specific parameters are included in CASTFOREM that allow BSA and BSAR to be used in DT logic to aid the C2 decision process. For example a command entity can query if a certain number of enemy units are positioned within a given grid reference as indicated by the perceived location of known enemy units. Such a feature in combination with configurable network architectures gives CASTFOREM the capability to model future battle field command and information exchange paradigms. A constraint is the extent to which a commander s decision process can be defined and implemented in decision tables which will be discussed in the next section. BSA and BSAR over the digital network were used to enhance C2 decision-making through the availability of a higher level of battlefield knowledge compared to that provided by the analogue system alone.

6 The current C2 structure is indicative of a future architecture rather than definitive. The main limitations of the representation are the assumption of perfect communications and the ability of commanders decision logic to deal with uncertainty. The modelling does achieve its aim of having a C2 architecture that is not an idealised linkage between commander and manoeuvre element or between sensor and actor. The architecture is flexible enough to demonstrate the trends in force effectiveness. The example in Figure 7 shows as Level 1 and Level 2 information improves there is an increasing trend in enemy losses over time in terms of tempo and total number. Enemy Losses Higher losses with higher threat information Higher rate of loss with higher threat information Time Figure 7: Impact of varying SA on Enemy losses Ideal High Medium Low 4.3 Command and Control The modelling of Command and Control (C2) is enabled by representative modelling of information and communication. Effective command and control is essential in achieving the commander s intent, by giving the entities on the battlefield direction. C2 is simulated by the integration of both the physical effects and human behaviour components of model as described in section 2. CASTFOREM models the C2 process of commanders at every level of the echelon by representing their decision processes in DT. Network-enabled battlefield coordination capability is implemented by sharing information between battlefield entities via explicit communication models. The resolution of the C2 modelling extends to the explicit passing of individual orders between individual entities. Each individual vehicle or platform was modelled and controlled by commanders at the various echelons. To add robustness to the C2 DT a command agent was developed to perform this role. There are a number of reasons for developing both individual entities and command agents. The unit level behaviour is determined by the ongoing interactions among its members, it is not the simple sum of the parts. Also, if a model were to combine a number of individual agents into an organisational unit and have them interact in a realistic way, then they probably would not exhibit organisational behaviour because of a lack of structural knowledge that plays such a large role in determining unit behaviour (Pew and Mavor 1998). By using command agents a lot of the detail necessary to achieve representative unit behaviour can be abstracted away from the individual agents and into a single control unit that moves with the echelon but does not take an active part in the combat. Just as an individual entity is a single platform, a command agent plays the role of an automated player who performs the C2 functions. This involves interpreting information and issuing orders to the units under its control, whether it be individual entities or aggregated units. Command agents are not represented as actual entities on the battle field because their presence is manifested in the actions of the units they control (Vaughan and Connell 2000). A command agent in CASTFOREM doubles as a position server for sending information across echelons (network structures). It serves as an entity to satisfy specific DT logic without having to develop complex reasoning to circumvent communication and C2 problems when platforms are destroyed (Young and Dzierzanowski 1999). 5 MODELLING FIDELITY In the field of modelling and simulation (M&S) fidelity is defined as being the level to which the simulation represents the source systems being examined. For the case of modelling human behaviour in simulations, fidelity refers to the exactness or accuracy to which the agent represents human behaviour. A term that is often used interchangeably with fidelity is resolution. For the purposes of this discussion resolution determines to what granularity the model represents human characteristics (Pace 1998). The fidelity of human behaviour modelling is determined by two factors. Firstly the resolution of the techniques used to model the components of human behaviour such as perception, working memory, cognition and motor behaviour. Secondly the architecture used to combine these models. Defining a scale of fidelity is difficult because it is a relative measure. For the purpose of discussion a high fidelity human behaviour model is one that accurately represents behaviour of a real individual in the context of the application. A low fidelity model only approximates the behaviour of an individual but still gives plausible reactions to a restricted set of events when viewed as a whole with other entities. It has been stated that there is no need to strive for completely realistic (that is high-fidelity) simulations of the battlefield. Instead, the emphasis is on simulating the environment to a sufficient level (that is, relative fidelity) to trigger tactically relevant responses (Tambe et al 1995). Based on the causal link between human behaviour modelling and physical actions (Figure 2), there is a positive feedback loop between digitisation modelling fidelity, a physical effect, and the fidelity of human

7 behaviour modelling in the simulation. The degree to which one effects the other is linked to the application of the model. In the case of CASTFOREM, a closed loop simulation being used to investigate the impact of the digitisation concept, as opposed to testing the actual digitisation systems, the link is relatively weak. Hence a large increase in the fidelity of digitisation modelling does not warrant an equivalent increase in the human behaviour modelling to achieve the desired level of investigation into the concept. The weak linkage is based on the goal to have a simulation where the entities are able to respond acceptably to unforeseen circumstances and deal with uncertain information as opposed to completely realistic reactions. The modelling architecture described in the preceding sections achieved this within a single specific scenario context by demonstrating the trends that result from digitisation using a relatively high fidelity physical model and relatively low fidelity human behaviour model. 6 INDIVIDUAL VS- UNIT LEVEL BEHAVIOUR Based on the description of fidelity in the previous section, it can be said that CASTFOREM has a low fidelity model of human behaviour since it only approximates the actions of an individual but still gives plausible reactions to a restricted set of events when viewed as a whole with other entities. The impact of battlefield digitisation on individual actions is enhancements in speed and accuracy, assuming the enhanced information is fully utilized. All are relatively simple to capture by modifying an individuals physical performance parameters. The impact of battlefield digitisation on unit level behaviour is more difficult to quantify. As described in the preceding sections, CASTFOREM uses command agents to model unit level behaviour. It is the unit level behaviour that will determine the effectiveness of the force as a whole due to the system synergies introduced by digitisation. To accurately represent such behaviour in a closed loop model it can be hypothesised that lower fidelity individual models and higher fidelity unit level models are required. To investigate digitisation in combat models the focus of effort should be in increasing modelling fidelity at the unit level and not the individual because the individual has a reduced impact on the group within a regimented C2 structure. 7 THE WAY FORWARD The question remains if the fidelity of the decisions captured during the constructive wargame were sufficient for the analysis conducted, especially in the case of using CASTFOREM for parametric excursions from the wargamed scenario. There are scenario boundaries, past which the behaviour modelled is no longer valid. In this case a constructive wargame must be revisited to capture the new set of decision criteria to be translated into decision tables. The alternative is to continue to develop and validate higher fidelity models of human behaviour, especially at the unit level, a trend already seen in the emergence of intelligent agents and related technologies. This will increase confidence in using the models for broader excursions from the baseline scenario. A balance must be struck between the effort required to conduct constructive wargames and the effort to develop and validate higher fidelity models. An emerging trend will be that as the unit level models improve there will be less reliance on constructive wargames, with their role being constrained to developing the baseline human behavioural characteristics and the dynamic aspects of the scenario before implementation in closed loop models such as CASTFOREM. 8 CONCLUSION The aim of the CASTFOREM analysis was to investigate the capabilities of a digitised force structure within a specific scenario context. Plausible unit level decision making was captured in a constructive wargame during the HE99 prior to implementation of the CASTFOREM model. The higher fidelity constructive wargame, with its human players, uncover system synergies and began to develop emergent behaviour. The CASTFOREM unit level behaviour representation, although at a lower fidelity than the wargame, was sufficient to replicate these actions in a constrained set of scenarios. The high fidelity digitisation models allowed the necessary degree of control to conduct parametric analysis. The resultant simulation allowed a trend analysis on key force effectiveness MOEs to be conducted. The analysis was based on the key assumptions that the human component involved in the application of the battlefield information was able to undergo a performance improvement in terms of doctrine and organisation. In addition it was assumed that the human system could process more information without degradation of performance. The future development of analytical simulations will not necessarily require high fidelity human behaviour models but ones that will provide a high degree of control and decrease development times. The level of fidelity is determined by the questions to be answered. The driver for development is the recognition that current models are inadequate in terms of development effort and that enhanced models will reduce development time and increase quality and depth of analysis. The challenge is to achieve this without introducing redundant complexity into the simulation that will make analysis more difficult. REFERENCES Australian Army (1999), Situational Awareness: Supporting Decision Superiority Army Trials Doctrine 7.1, Edition 2, Version 1

8 Bowley, D., Lovaszy, S. (1999), Use of Combat Simulations and Wargames in Analytical Studies, Proc SimTecT 99, Paper 11 Doctrine Wing CATDC (1999), Land Warfare Doctrine 1: The Fundamentals of Land Warfare Mackey, D. (1990), Knowledge Base Analysis, 30 th Joint National Meeting of the Operations Research Society of America and The Institute of Management Science Pace, D.K. (1998) Verification, Validation and Accreditation, In Cloud, D.J. & Rainey, L.B., Editors Applied Modelling and Simulation: An Integrated Approach to Development and Operation McGraw-Hill, New York, NY, pp Pew, R.W., Mavor, A.S. (Editors) (1998), Modeling Human and Organizational Behavior: application to military simulations, National Academy Press, Washington, D.C Tailby, D., Stanford, C., Cause, R., Pash, K., Jose, A.(2001), CASTFOREM Modelling of the Enhanced Combat Force, DSTO Research Report (in preparation) Tambe, M.W., Johnson, W.L., Jones, R.M., Koss, F., Laird, J.E., Rosebloom, P.S., & Schwamb, K. (Spring 1995), Intelligent Agents for Interactive Simulation Environments AI Magazine, Vol 16 (no 1), pp Vaughan, J. & Connell, R. (2000) Combining Intelligent Agents and Artificial Intelligence for the Land Force Proceedings of the SIMTECT Conference, Sydney Australia, 28 February 2 March, published on CD-ROM Young, K., Dzierzanowski, K. (1999), Assessing Task Force Digitisation, US Army TRADOC Analysis Center White Sands Missile Range