AAAI Publications, Sixth European Conference on Planning

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Combining Two Fast-Learning Real-Time Search Algorithms Yields Even Faster Learning
David Furcy, Sven Koenig

Last modified: 2014-05-21

Abstract


Real-time search methods, such as LRTA*, have been used to solve awide variety of planning problems because they can make decisions fastand still converge to a minimum-cost plan if they solve the sameplanning task repeatedly. In this paper, we perform an empiricalevaluation of two existing variants of LRTA* that were developed tospeed up its convergence, namely HLRTA* and FALCONS. Our experimentalresults demonstrate that these two real-time search methods havecomplementary strengths and can be combined. We call the new real-timesearch method eFALCONS and show that it converges with fewer actionsto a minimum-cost plan than LRTA*, HLRTA*, and FALCONS.

Keywords


heuristic search, real-time search

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