State Abstraction in Real-time Heuristic Search: The Director’s Cut
نویسندگان
چکیده
Real-time heuristic search methods are used by situated agents in applications that require the amount of planning per move to be independent of the problem size. Such agents plan only a few actions in a local search space and avoid getting trapped in local minima by improving their heuristic function over time. We extend a wide class of real-time search algorithms with automatically built state abstraction. We then prove completeness and convergence of the resulting novel family of algorithms. Finally, we analyze effects of abstraction in an extensive empirical study situated in the goal-directed navigation problem. In particular, we demonstrate the new algorithms are most efficient in trading pairs of antagonistic performance measures such as response time and overall convergence speed.
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