Multi-Perspective Enemy Course of Action Maintenance

Transcription

1 Distribution ment A: Approved for public release; distribution is unlimited. Multi-Perspective Enemy Course of Action Maintenance June 10, 2004 William J. Farrell III, Benjamin Grooters, and Frank Vetesi Lockheed Martin Advanced Technology Laboratories Cherry Hill, New Jersey [wfarrell, bgrooter, ABSTACT Estimation of the Enemy s Course of Action (ECA) is required for assessing enemy intent and mission impact, both of which are key functional components embodied in the Joint Directors of Laboratories (JDL) Level (Threat efinement) processing description. Most ECA estimation approaches assume that the set of ECA hypotheses are enumerated in advance and therefore reduce the problem of ECA maintenance to a hypothesis test performed over time. However, when asymmetric threats are present, ECA hypotheses cannot generally be enumerated in advanced and therefore need to be dynamically generated, decomposed, and evaluated in near real-time. Lockheed Martin Advanced Technology Laboratories has developed a multi-perspective approach to ECA maintenance that employs Threat Perspective Models for the generation, decomposition, and evaluation of ECA hypotheses. Each Threat Perspective Model generates hypotheses based upon feasibility and utility, decomposes hypotheses into sub-tasks using a Hierarchical Task Network (HTN), and evaluates hypotheses based upon observations of ECA sub-tasks. This approach is justified by a goal-directed task analyses performed on Army Intelligence Analysts, which indicate that human analysts maintain ECA hypotheses by viewing the battlefield from multiple perspectives such as terrain, weather, armament, doctrine, etc. This multiperspective approach makes the ECA maintenance problem more tractable and allows for seamless incorporation of new Threat Perspective Models. The multiperspective ECA maintenance approach presented in this paper is being incorporated into Army Fusion &D programs aimed at providing Level Fusion at multiple echelons. 1. Motivation Military analysts expend an extraordinary amount of resources and manpower attempting to determine the intended actions of an adversary. Even with conventional adversaries, where tactics and capabilities are typically well known, analysts struggle to predict the actions that an adversary might take. As the battle space evolves and becomes occupied by creative, asymmetric threats whose tactics and capabilities are not well known, the military analyst is faced with a nearly impossible task.

2 To aid the military analyst, significant effort has been focused on understanding the process by which human analysts attempt to predict adversary actions [1]. Lockheed Martin Advanced Technology Laboratories (ATL) has developed an architecture for estimating the Enemy Course of Action (ECA) using a multi-perspective approach [2]. ATL's approach is based on the realization that military analysts perform their analysis from various perspectives and combine the results to arrive at a conclusion. This is typically a manual process performed using a sequence of tactical overlays. As an illustration, an analyst may create a tactical overlay of the battle space terrain on a map. Then, another analyst may construct an additional overlay indicating cultural structures such as military facilities. Finally, another analyst may generate an overlay depicting a threat region by considering the enemy s likely weaponry. Collecting all of these overlays, the analyst attempts to deduce a potential ambush location. The Multi-Perspective ECA Maintenance approach outlined in this paper attempts to mimic the cognitive tasks performed by military analysts. 2. Background Concepts This section introduces the definitions and concepts that will be employed throughout the remainder of this paper. First, a definition and representation for an Enemy Course of Action (ECA) is presented. Secondly, the Hierarchical Task Network (HTN) is introduced as a framework for ECA Decomposition. Finally, this section presents the concept of Threat Perspective Models. 2.1 Enemy Course of Action epresentation An Enemy Course of Action (ECA) is a planned task performed by an adversary. An ECA can be represented as a triplet []. This triplet consists of a esource, an Action Verb, and an bjective. The semantics of the ECA triplet are (Figure 1): A esource performs an Action Verb focused on an bjective. As an example, an ECA might be: The 52 nd Infantry Division (esource) secures (Action Verb) Hill 12 (bjective). In the context of this paper, it is assumed that the esource is always an adversary entity while the bjective is assumed to be a friendly asset. This representation of an ECA is used throughout the remainder of this paper. ESUCE PEDICATE BJECTIVE PEDICATE ESUCE performs ACTIN VEB focused on BJECTIVE E5 E2 E9 AV 1 AV 2 E4 E8 AV AV 4 E Adversary Aggregate AV 5 AV n Feasible Set of Actions Friendly Asset (Assumed Known) Figure 1. ECA epresentation and Semantics 2

3 2.2 Hierarchical Task Networks Figure 1 is the simplest ECA that can be represented. It consists of only one task. Depending upon the military echelon, this description may be insufficient. For example, a commander of theatrewide operations may find that an ECA such as The 52 nd Infantry Division secures Hill 12 is sufficiently useful to support planning and decision-making. However, at the platoon echelon, this ECA is insufficient for planning friendly actions and responses. Because the required specificity of an ECA can vary with military echelon, the ECA representation should also be capable of various levels of specificity. A construct known as the Hierarchical Task Network (HTN) provides a representation of tasks at various levels of specificity [4]. Figure 2 illustrates an example of an HTN. The HTN not only mimics the variation in specificity found in military echelons, it also allows a computational construct for analyzing ECAs. In the Multi-Perspective ECA Maintenance algorithm presented in Sections 4-5, the HTN is employed to provide a method for decomposing high-level ECAs into more specific tasks. Directly Engage Enemy Locate Enemy Move to Contact Fire on Enemy Move to iver Cross Bridge Use Tunnel Move to Contact Figure 2. Decomposable tasks represented using a Hierarchical Task Network The HTN representation is the basis of most modern planning algorithms. It is based on the concept that humans plan by decomposing tasks into smaller ones until a sequence of atomic tasks are found that satisfy the objective. Within the context of this paper, the atomic tasks are the tasks that the Fusion processes attempt to infer or observe directly. 2.2 Threat Perspective Models As mentioned in Section 1, ATL has developed an approach to ECA maintenance that attempts to mimic the military analysts adversarial reasoning process [1]. Human analysts examine the state of the battle space from multiple perspectives, such as naval, land, air, weather, etc. Subsequently, ECAs are generated and decomposed using each perspective. Then, using inferred results from each perspective, the likelihood of each ECA is evaluated.

4 In order to mimic and automate the human process, a construct for a perspective must be developed. These constructs are called Threat Perspective Models. The functional role of a Threat Perspective Model (TPM) is to provide support for ECA maintenance by examining a subset of the battle space environment. A TPM assists in the generation, decomposition, and evaluation of ECA hypotheses. The precise implementation of a TPM (Bayesian Network, ule-based Expert System, etc) is not of particular concern. TPMs allow ECA maintenance to be simplified by partitioning and decoupling the analysis. TPMs come in two different varieties (perhaps indicating the specifics of the implementation). First, there are Fundamental Threat Perspective Models. These models consider physics-based aspects of the environment such as mobility, weather, weapons effectiveness, etc. The Fundamental TPMs are used to evaluate the feasibility of possible ECAs by imposing quantifiable constraints on the adversary s actions. Secondly, there are Domain Threat Perspective Models. These models consider domain-specific aspects of the environment such as armament, logistics, tactics, doctrine, etc. The Domain TPMs are used to evaluate the utility of ECAs by applying domain-specific rules to the entities observed within the battle space. As presented in the following sections, TPMs assist in the ECA maintenance process in several ways, but always with a restricted view of the environment.. ECA Generation The first requirement for Multi-Perspective ECA Maintenance is the ability to generate ECA hypotheses. This step is similar to the process of Track Initiation in Level 1 Fusion [5]. Here, the current state of friendly/adversary forces and friendly objectives/goals are used to generate ECA hypotheses (Figure ). ECA HYPTHESES Adversary (ED) perational Picture ECA Generation X X Friendly (BLUE) perational Picture X Friendly (BLUE) bjectives/s Figure. Generation of ECA Hypotheses Fundamental TPMs provide measures of Feasibility while Domain TPMs provide measures of Utility in order to rank and prune the hypothesis set (Figures 4-5). 4

5 BJECTIVE PEDICATE ESUCE performs ACTIN VEB focused on BJECTIVE TACTICAL EFFECTS BJECTIVE VALUE ACTIN VEBS BJECTIVES BJECTIVE PEDICATES UTILITY anking/pruning uses TACTICAL EFFECTS and BJECTIVE VALUE Figure 4. anking ECAs using Utility Measures provided by Domain Threat Perspective Models ESUCE PEDICATE BJECTIVE PEDICATE ESUCE performs ACTIN VEB focused on BJECTIVE ESUCE HLDINGS ACTIN EQUIED CAPABILITIES TACTICAL EFFECTS BJECTIVE VALUE ESUCES BJECTIVE PEDICATES ECA HYPTHESES FEASIBILITY anking/pruning uses ACTIN EQUIED CAPABILITIES and ESUCE HLDINGS 1 5 Figure 5. anking ECAs using Feasibility Measures provided by Fundamental Threat Perspective Models The order in which the ranking of hypotheses is performed (Feasibility-Utility or Utility-Feasibility) can dramatically affect the results of this process. To mitigate this sensitivity, a method such as Fuzzy Partial rdering [5] should be applied. This approach avoids the pitfalls of simple linear ordering approaches. In particular, each TPM may apply a Fuzzy Inference approach to assess the set membership for Feasible or Useful and then Fuzzy Partial rdering can be applied to rank the results using the Boolean criteria Feasible and Useful. The set of ECA hypotheses resulting from this process are decomposed and evaluated. In addition, the ECA generation process is re-invoked (either entirely or in part) whenever incoming information does not support any currently available ECA hypothesis. Again, this general procedure mimics Track Initiation (Level 1 Fusion) where measurements are used to initiate a new track when they do not associate with any existing tracks. 5

6 4. ECA Decomposition nce the ECA hypotheses have been generated from the process outlined in Section, the ECA Maintenance system must evaluate them. However, since the generated hypotheses are not directly observable, they are not suitable for hypothesis testing. As with any hypothesis test, observables must be identified. These observables act as indicators to refute or support ECA hypotheses. A Hierarchical Task Network (HTN) planner [4] is employed to decompose ECA hypotheses into observable task sequences. The Threat Perspective Models play an important role within the ECA Decomposition process (Figure 6). They are responsible for observing (inferring) certain tasks within the HTN. For example, a Mobility Threat Perspective Model might be responsible for observing the task A move to B for an arbitrary pair of entities A and B. In effect, the Threat Perspective Models define which tasks are considered to be atomic. The ECA Decomposition can be performed in a sequential manner, performing decomposition using only the Domain Threat Perspective Models followed by decomposition using only the Fundamental Threat Perspectives. AV activate radar Domain Threat Perspective Models Logistics Armament Fundamental Threat Perspective Models Mobility Weather reach area #1 reach area #2 pass north of lake pass south of lake cross bridge activate radar pass north of mountain reach area #1 reach area #2 Figure 6. Sequential ECA Decomposition using Threat Perspective Models nce the ECA hypotheses have been decomposed into observable tasks, it is the responsibility of the Threat Perspective Models to evaluate the current belief that the observable tasks are being performed. 5. ECA Evaluation The final major component of the proposed Multi-Perspective ECA Maintenance approach is ECA Evaluation. Analogous to Level 1 Fusion, this is the Track Maintenance process [5] where the system attempts to associate observed information with each ECA hypothesis over time. The goal is to determine which ECA hypothesis most strongly supports the observed information. Again, the TPMs play an important role in the evaluation process. Each TPM is responsible for inferring and/or observing a sub-set of tasks within each decomposed ECA hypothesis. Together, the TPMs collaborate, much as military analysts exchange information, in order to evaluate the current belief of each ECA hypothesis. Figures 7-8 illustrate this process by example. 6

7 Fundamental Perspective Models Mobility Weather pass north of lake cross bridge #1 mountain lake cross bridge #2 pass north of mountain Domain Perspective Models Logistics Armament reach area #1 pass south of lake reach area #2 pass north of lake cross cross pass south pass south bridge #1 bridge #2 of mountain activate of lake reach radar area # breach river Figure 7. Threat Perspective Models contributing to ECA Evaluation Fundamental Perspective Models Mobility Weather pass south of lake cross bridge #1 mountain lake cross bridge #2 pass north of mountain Domain Perspective Models Logistics Armament reach area #1 reach area #2 activate radar pass south of lake cross cross pass south bridge #1 bridge #2 of mountain reach area # Figure 8. Threat Perspective Models contributing to ECA Evaluation In Figure 7, there are two hypothetical ECAs being evaluated. After some time has elapsed, the Fundamental Threat Perspective Models have inferred the actions taken by the adversary: 1) Passed South of Lake, and 2) Crossed Bridge #1. At this point, both ECAs seem equally likely because no discriminating information has been obtained. However, as time elapses further (Figure 8), the Threat Perspective Models contribute additional inferences: 1) Crossed Bridge #2, 2) Passed South of Mountain, and ) Enemy Activated adar. At this point, the second ECA appears more likely. In general, the role of the Threat Perspective Models is to monitor the atomic tasks generated by the HTN planner (see Section 4). Subsequently, ECA hypotheses can be evaluated and the belief in each hypothesis is revised. 7

8 ne quantitative approach to maintaining ECA beliefs employs Fuzzy Finite Machines (FFSM) [5]. In particular, the HTN (see Section 4) used for ECA Decomposition is cast into a hierarchical network of Fuzzy Finite Machines representing the causal-temporal relationships between tasks. By maintaining the state of these Fuzzy Finite Machines, the beliefs for each ECA hypothesis is maintained. Figure 9 illustrates the hierarchical FFSM used for ECA Evaluation. Fuzzy Finite Machines ECA t t2 t Hierarchical Task Network Figure 9. Hierarchical Fuzzy Finite Machines for ECA Evaluation The steps for ECA Evaluation using Hierarchical FFSMs are: 1. Transition Possibilities: Compute transition possibilities for each node 2. Predict FFSM: Predict Fuzzy states for each node. Modus Tollen: Make partial observations using modus tollens logic 4. Fuzzy Innovations: Compute fuzzy innovations for the FFSM 5. Update Fuzzy s: Compute revised states in the FFSM This process is reminiscent of Kalman Filtering with the exception of the observation process. Since the observations made about the states of the FFSM are logical, Modus Tollens is applied in replacement of the Kalman bservation function. Transition Possibilities are computed for each node in the FFSM. These possibilities represent the belief that the FFSM will remain in a given node's state. A non-zero transition possibility represents belief leaving a given node in the FFSM. These transition possibilities are denoted by: τ ( t s, xˆ ) µ (1) where s is the state represented by a node in the FFSM and xˆ is the current state of the enemy. As an example, one of the states (s=1) in the FFSM may be: <Time-to-Go> is <Long> (2) and the current state of the enemy is: [ ange angeate ] T x ˆ = () then, the transition possibility conditioned on the FFSM state (Equation 2) and the enemy state (Equation ) is: 8

9 τ ( t s = 1, xˆ = [ ange angeate ] ) T µ (4) which is the Sojourn Time [6] for remaining in the state represented by Equation 2. FFSM prediction is accomplished by Fuzzy Functional Composition [5]. The equation for predicting the FFSM state into the future is given by: (5) where S is the FFSM state vector, µ τ is the transition possibility, and δ represents a perturbative input into the system over the prediction interval. The perturbative input can be used to represent changes in external states such as friendly actions. Modus Tollens is now applied to make observations of the FFSM. Modus Tollens is the procedure of logical backward inference. This step is analogous to computing the Likelihood Function in a Kalman filter. Here, however, the observation process is logical: Sˆ t xˆ ) = if ( s) is < Possible > (6) then ( observation is < Y > The Threat Perspective Models contribute, once again, to ECA Maintenance by performing Modus Tollens. nce observations have been made, the Fuzzy Innovations are computed. These innovations measure the degree to which the predicted FFSM states (Equation 5) agree with the observations (Equation 6). The Fuzzy Innovations are computed by: m S ~ St xˆ t Sˆ = (7) and the null state residual is: t (8) Finally, to complete an iteration of the FFSM update, the Fuzzy states are updated according to: ˆ + S k 1 = St m~ + (9) S t and the null state update equation is: (10) Equations 1 through 10 outline the steps for performing an update of a FFSM. The hierarchical FFSMs illustrated in Figure 9 are updated in this fashion. Thus, an approach for sequential ECA hypothesis belief maintenance is presented. 9

10 6. Conclusions Driven by a -Directed Task Analysis of the military intelligence analyst, Multi-Perspective ECA Maintenance attempts to mimic the problem solving approach employed by these analysts. The Multi- Perspective ECA Maintenance approach has three functional components: ECA Generation, ECA Decomposition, and ECA Evaluation. Supporting each of these components are models called Threat Perspective Models that provide restricted view of the battle space environment. The use of these Threat Perspective Models not only mimics the human military analyst, but also allows for a simplified computational architecture. In addition to the Multi-Perspective ECA Maintenance architecture presented in this paper, a novel approach to ECA hypothesis belief maintenance based upon hierarchical Fuzzy Finite Machines was presented. eferences [1] SA Technologies. Directed Task Analysis of the Brigade Intelligence fficer, Fusion Based Knowledge for the bjective Force, June 200. [2] Angela Pawlowski, Sergio Gigili, and Frank Vetesi. Situation and Threat efinement Approach for Combating the Asymmetric Threat, Military Sensing Symposia, National Symposium on Sensor and Data Fusion 2002, San Diego, CA, August 1-15, [] North Atlantic Treaty rganization. The Land C2 Information Exchange Data Model, ATCCIS Baseline 2.0, March 18, [4] K. Erol, D. Nau, J. Hendler. HTN Planning: Complexity and Expressivity In AAAI-94, Seattle, July, [5] S. Blackman, Design and Analysis of Modern Tracking Systems, Artech House, [6] Y. Bar-Shalom, X.. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation, Jon Wiley & Sons, 2001, pp &