Beating the Defense: Using Plan Recognition to Inform Learning Agents

Molineaux, Matthew (Knexus Research Corporation) | Aha, David W. (Naval Research Laboratory) | Sukthankar, Gita (University of Central Florida)

AAAI Conferences 

In this paper, we investigate the hypothesis that plan recognition can significantly improve the performance of a case-based reinforcement learner in an adversarial action selection task. Our environment is a simplification of an American football game. The performance task is to control the behavior of a quarterback in a pass play, where the goal is to maximize yardage gained. Plan recognition focuses on predicting the play of the defensive team. We modeled plan recognition as an unsupervised learning task, and conducted a lesion study. We found that plan recognition was accurate, and that it significantly improved performance. More generally, our studies show that plan recognition reduced the dimensionality of the state space, which allowed learning to be conducted more effectively. We describe the algorithms, explain the reasons for performance improvement, and also describe a further empirical comparison that highlights the utility of plan recognition for this task.

Duplicate Docs Excel Report

None found

Similar Docs  Excel Report  more

None found