SetA*: an efficient BDD-based heuristic search algorithm

  • Authors:
  • Rune M. Jensen;Randal E. Bryant;Manuela M. Veloso

  • Affiliations:
  • Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA;Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA;Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA

  • Venue:
  • Eighteenth national conference on Artificial intelligence
  • Year:
  • 2002

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Abstract

In this paper we combine the goal directed search of A* with the ability of BDDs to traverse an exponential number of states in polynomial time. We introduce a new algorithm, SetA*, that generalizes A* to expand sets of states in each iteration. SetA* has substantial advantages over BDDA*, the only previous BDD-based A* implementation we are aware of. Our experimental evaluation proves SetA* to be a powerful search paradigm. For some of the studied problems it outperforms BDDA*, A*, and BDD-based breadth-first search by several orders of magnitude. We believe exploring sets of states to be essential when the heuristic function is weak. For problems with strong heuristics, SetA* efficiently specializes to single-state search and consequently challenges single-state heuristic search in general.