Intent Inference Using a Potential Field Model of Environmental Influences Robin Glinton, Sean Owens, Joseph Giampapa, Katia Sycara Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 U.S.A. rglinton,owens,garof, katia@cs.cmu.edu Michael Lewis, Chuck Grindle School of Information Sciences University of Pittsburgh Pittsburgh, PA 15213 U.S.A ml,cgrindle@mail.sis.pitt.edu Abstract -Intent inferencing is the ability to predict an opposing force's (OPFOR) high level goals. This is accomplished by the interpretation of the OPFOR’s disposition, movements, and actions within the context of known OPFOR doctrine and knowledge of the environment. For example, given likely OPFOR force size, composition, disposition, observations of recent activity, obstacles in the terrain, cultural features such as bridges, roads, and key terrain, intent inferencing will be able to predict the opposing force's high level goal and likely behavior for achieving it. This paper describes an algorithm for intent inferencing on an enemy force with track data, recent movements by OPFOR forces across terrain, terrain from a GIS database, and OPFOR doctrine as input. This algorithm uses artificial potential fields to discover field parameters of paths that best relate sensed track data from the movements of individual enemy aggregates to hypothesized goals. Hypothesized goals for individual aggregates are then combined with enemy doctrine to discover the intent of several aggregates acting in concert. Keywords: Intent inference, artificial potential field, information fusion. 1 Introduction In the military domain, adversarial intent inference is traditionally achieved by the manual fusion of heterogeneous sources of information. These sources include textual reports, maps, and low level sensor fusion products like force aggregates. Moreover, it is the people that are fusers providing additional background knowledge in the process. Because of the increasing availability of cheap sensors and the maturation of network technology, analysts have timely access to terabytes of high fidelity information about battlefield state. This has created cognitive overload. As a result, it is becoming increasingly difficult to fuse this low level information and extract useful inferences about enemy intent from it quickly enough to positively influence the decisions of military commanders. The battlefield is a noisy, uncertain, and despite increasingly available networked sensors, still only partially observable environment. Many of the proposed approaches to adversarial intent inference which rely on recognition of tactical maneuvers e.g. [1] use Bayesian techniques that encode team maneuvers by statistics on low level information like the velocities and trajectories of individual team members while they are executing a particular strategy. When faced with a novel situation these statistics are used to calculate the posterior probability that the team is executing a certain maneuver. Such statistical techniques have proven effective at recognizing team strategies in sports [1]. However, it is unlikely that such techniques would be effective in the uncertain, dynamic, partially observable, and noisy environment of a battlefield. In team sports there are a small number of players and the movements of all of them are visible at all times. There are also a few clearly defined objectives and the terrain is usually featureless. In contrast military operations are conducted in a variety of terrains with a myriad of objectives both concrete and abstract each of which could have many sub goals necessary to achieve them. Furthermore, in the military domain hundreds of