Justin Karneeb

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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 the agent that controls it can intelligently plan. However, planning effectively in an air combat scenario requires understanding the behaviors of hostile agents in that scenario, which is challenging in partially observable environments such as the one we(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)
One of the central problems in spatial language understanding is the polysemy and the vagueness of spatial terms, which cannot be resolved by lexical knowledge alone. We address this issue by developing a representation framework for functional interactions between objects and agents. We use this framework with a constraint solver to resolve and recover the(More)
Accurately modeling uncontrolled agents (or recognizing their behavior and intentions) is critical to planning and acting in a multi-agent environment. However, behavior recognition systems are only as good as their observations. Here we argue that acting, even acting at random, can be a critical part of gathering those observations. Furthermore, we claim(More)
Object placement in virtual and real worlds is an important task for autonomous agents and applications. Interacting with agents using natural language commands presents an intuitive alternative to graphical and other operator control interfaces. However, understanding and interpreting language for placement actions in 3D continuous spaces is(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)
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