An algorithm better than AO*?

  • Authors:

  • Blai Bonet;Héctor Geffner

  • Affiliations:

  • Departamento de Computación Universidad Simón Bolívar, Caracas, Venezuela;ICREA & Universitat Pompeu Fabra, Barcelona, Spain

  • Venue:
  • AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
  • Year:
  • 2005

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Abstract

Recently there has been a renewed interest in AO* as planning problems involving uncertainty and feedback can he naturally formulated as AND/OR graphs. In this work, we carry out what is prohably the first detailed empirical evaluation of AO* in relation to other AND/OR search algorithms. We compare AO* with two other methods: the well-known Value Iteration (VI) algorithm, and a new algorithm, Learning in Depth-First Search (LDFS). We consider instances from four domains. usc three different heuristic functions, and focus on the optimization of cost in the worst case (Max AND/OR graphs). Roughly we find that while AO* does better than VI in the presence of informed heuristics, VI does better than recent extensions of AO* in the presence of cycles in the AND/OR graph. At the same time, LOFS and its variant Bounded LOFS, which can be regarded as extensions of IDA*, are almost never slower than either AO* or VI, and in many cases, are orders-of-magnitude faster.