Using Infeasibility to Improve Abstraction-Based Heuristics

  • Fan Yang
  • Joseph Culberson
  • Robert Holte
Conference paper

DOI: 10.1007/978-3-540-73580-9_41

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4612)
Cite this paper as:
Yang F., Culberson J., Holte R. (2007) Using Infeasibility to Improve Abstraction-Based Heuristics. In: Miguel I., Ruml W. (eds) Abstraction, Reformulation, and Approximation. SARA 2007. Lecture Notes in Computer Science, vol 4612. Springer, Berlin, Heidelberg

Abstract

The contribution of our research is to show that the accuracy of the heuristics generated by abstraction can be improved by checking for infeasibility. What do we mean by infeasible heuristics? For a state t, the heuristic value h is infeasible if it is proved that the cost of a solution for t cannot be h. Take the sliding puzzle for example, assuming that the manhattan heuristic for state t is md(t), if md(t) is even, any odd number is infeasible. To substantiate our approach, we begin with formal definitions and lemmas. Then empirical results show the effectiveness of the approach. For more details please refer to our longer work[5].

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fan Yang
    • 1
  • Joseph Culberson
    • 1
  • Robert Holte
    • 1
  1. 1.Computing Science Department, University of Alberta, Edmonton, Alberta T6G 2E8Canada

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