Case-Based Team Recognition Using Learned Opponent Models

For an agent to act intelligently in a multi-agent environment it must model the capabilities of other agents. In adversarial environments, like the beyond-visual-range air combat domain we study in this paper, it may be possible to get information about teammates but difficult to obtain accurate models of opponents. We address this issue by designing an agent to learn models of aircraft and missile behavior, and use those models to classify the opponents' aircraft types and weapons capabilities. These classifications are used as input to a case-based reasoning (CBR) system that retrieves possible opponent team configurations (i.e., the aircraft type and weapons payload per opponent). We describe evidence from our empirical study that the CBR system recognizes opponent team behavior more accurately than using the learned models in isolation. Additionally, our CBR system demonstrated resilience to limited classification opportunities, noisy air combat scenarios, and high model error.