LRTA* Works Much Better with Pessimistic Heuristics

Authors:
Aleksander Sadikov;Ivan Bratko
Affiliations:
Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia, e-mail: aleksander.sadikov@fri.uni-lj.si;Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia, e-mail: ivan.bratko@fri.uni-lj.si
Venue:
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Year:
2008

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Abstract

Recently we showed that under very reasonable conditions, incomplete, real-time search methods like RTA* work better with pessimistic heuristic functions than with optimistic, admissible heuristic functions of equal quality. The use of pessimistic heuristic functions results in higher percentage of correct decisions and in shorter solution lengths. We extend this result to learning RTA* (LRTA*) and demonstrate that the use of pessimistic instead of optimistic (or mixed) heuristic functions of equal quality results in much faster learning process at the cost of just marginally worse quality of converged solutions.