The Statistical Inference Method in Heuristic Search Techniques

In this paper we present a new heuristic searching algorithm by introducing statistical inference method on the basic of algorithm A*. It's called algorithm SA*. The following results have been proved. (1) Algorithm SA* in superior to algorithm A*. (2) The mean complexity of SA* is CN2, but in some case A* exhibits exponential complexity (eN). (3) In a (N,d,F)- game tree, the mean complexity of SA* is CN2, but the complexity of other known game-searching algoritham (α-β , SSS* etc.) is at least dN. (4) The maximal storage-space required by SA* is C,N. This shows that under a given significance level SA* is superior to other known algorithm (e.g. A*, B*, α-β, SSS* etc.).

[1]  Judea Pearl,et al.  Asymptotic Properties of Minimax Trees and Game-Searching Procedures , 1980, Artif. Intell..

[2]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Vipin Kumar,et al.  Branch & Bound Formulation for Sequential and Parallel Game Tree Searching: Preliminary Results , 1981, IJCAI.

[4]  Gérard M. Baudet,et al.  On the Branching Factor of the Alpha-Beta Pruning Algorithm , 1978, Artif. Intell..

[5]  Judea Pearl,et al.  Heuristic Search Theory: Survey of Recent Results , 1981, IJCAI.

[6]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[7]  George C. Stockman,et al.  A Minimax Algorithm Better than Alpha-Beta? , 1979, Artif. Intell..