State Abstraction in Real-Time Heuristic Search

Abstract

Real-time heuristic search methods, such as LRTA*, 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 at a time in a local search space and avoid getting trapped in local minima by improving their heuristic function over time. In this talk we present recent extensions to LRTA* based on automated state abstraction – an idea that has proved powerful in other areas of search and learning. In one of the extensions, learning performance of LRTA* is improved by running it in a smaller abstract search space. The resulting algorithm retains real-time performance and completeness/ convergence properties. Empirically, the abstraction is found to improve efficiency by trading off planning time, learning speed and other antagonistic performance measures. The talk will be illustrated with applications to path-planning in computer video games.