Justin Karneeb

Case-Based Policy and Goal Recognition

We present the Policy and Goal Recognizer (PaGR), a case-based system for multiagent keyhole recognition. PaGR is a knowledge recognition component within a decision-making agent that controls simulated unmanned air vehicles in Beyond Visual Range combat. PaGR stores in a case the goal, observations, and policy of a hostile aircraft, and uses cases to recognize the policies and goals of newly-observed hostile aircraft. In our empirical study of PaGR’s performance, we report evidence that knowledge of an adversary’s goal improves policy recognition. We also show that PaGR can recognize when its assumptions about the hostile agent’s goal are incorrect, and can often correct these assumptions. We show that this ability improves PaGR’s policy recognition performance in comparison to a baseline algorithm.

A Goal Reasoning Agent for Controlling UAVs in Beyond-Visual-Range Air Combat

We describe the Tactical Battle Manager (TBM), an intelligent agent that uses several integrated artificial intelligence techniques to control an autonomous unmanned aerial vehicle in simulated beyond-visual-range (BVR) air combat scenarios. The TBM incorporates goal reasoning, automated planning, opponent behavior recognition, state prediction, and discrepancy detection to operate in a real-time, dynamic, uncertain, and adversarial environment. We describe evidence from our empirical study that the TBM significantly outperforms an expert-scripted agent in BVR scenarios. We also report the results of an ablation study which indicates that all components of our agent architecture are needed to maximize mission performance.

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.