Team 8: Enhancing the Combat ID Agent Based Model

During previous Project Albert and International data Farming Workshops (IDFW) and during discussions between Dstl and TNO, the suitability and feasibility of Agent Based Models (ABMs) to support research on Combat Identification (Combat ID) was examined. The objective of this research is to: Investigate the effect of (a large number of) different variations in Situational Awareness, Situation Awareness (SA), Target Identification (Target ID), Human Factors, and Tactics, Techniques, and Procedures (TTP) under different circumstances (scenarios) on mission level combat effectiveness and fratricide. Combat ID is a complex phenomenon which is heavily based on human factors, technology and tactical considerations. Modeling Combat ID to its full extent is not possible in a single step. It requires both a good combat model and a representation of the Target Detection, Classification, Identification process that takes the considerations mentioned above into account. As a first step to support our objectives, we decided to evaluate the feasibility to represent Situation Awareness in an ABM. This evaluation was conducted during IDFW14. Before and during this workshop, version 1.0 was developed in NETLOGO. This model contains one moving identifying agent and a number of static agents to be identified (objects). The identifying agent has a representation of situation Awareness (SA) and bases its identification decision on a mechanism where it combines SA and data from observations. Current Features and Objectives Following our overall Master plan, several extensions have been implemented since IDFW14: 1. When the identifying agent has not decided on the identity of a certain visited object, it is able to revisit the object and try to decide on its identity again. 2. When the agent decides that an object is an enemy, it kills the object. The object will then be removed from the ground truth. 3. The notion of Local SA and Global SA was introduced. Global SA keeps track of the pre-conception of the whole environment in which the agent operates. Its’ granularity is less than the granularity of the ground truth. Local SA keeps track of the agents’ preconception of its’ surrounding area. The granularity of Local SA is equal to the granularity of the ground truth. The size of the local SA and the granularity of the global SA are parameterized (and thus data farmable). The local SA is updated each time new sensor information is accepted or as a result of moving. When the agent moves, the local SA grids moves with it, keeping the agent in the middle of it. As a result of the move, some cells will be removed from the local SA and new cells are added, taking the belief distribution of the global SA cell as its’ initial belief. The global SA is updated each time the agent decides on the identity of an object. Figure 1 shows the relation between the Local SA, the Global SA and the ground truth. The objectives of the study during IDFW15 were to assess the features above by designing and conducting data farming experiments. Further objectives were to (re-)examine and determine the key factors (parameters) in SA.