Justin Karneeb

Learn More
In this paper we study the topic of CBR systems learning from observations in which those observations can be represented as stochastic policies. We describe a general framework which encompasses three steps: (1) it observes agents performing actions, elicits stochastic policies representing the agents' strategies and retains these policies as cases. (2)(More)
An unmanned air vehicle (UAV) can operate as a capable team member in mixed human-robot teams if it is controlled by an agent that can intelligently plan. However, planning effectively in a beyond-visual-range air combat scenario requires understanding the behaviors of hostile agents, which is challenging in partially observable environments such as the one(More)
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(More)
  • 1