Chapter

Abstraction, Reformulation, and Approximation

Volume 4612 of the series Lecture Notes in Computer Science pp 413-414

Using Infeasibility to Improve Abstraction-Based Heuristics

  • Fan YangAffiliated withComputing Science Department, University of Alberta, Edmonton, Alberta T6G 2E8
  • , Joseph CulbersonAffiliated withComputing Science Department, University of Alberta, Edmonton, Alberta T6G 2E8
  • , Robert HolteAffiliated withComputing Science Department, University of Alberta, Edmonton, Alberta T6G 2E8

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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].