AAAI Publications, Fourth Annual Symposium on Combinatorial Search

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Distance Learning in Agent-Centered Heuristic Search
Nathan R. Sturtevant

Last modified: 2011-07-05

Abstract


Real-time agent-centric algorithms have been used for learning and solving problems since the introduction of the LRTA* algorithm in 1990. In this time period, numerous variants have been produced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. This short paper discusses the history and implications of learning g-costs, both alone and in conjunction with learning h-costs as an introduction to the new f-LRTA* algorithm which learns both.

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