Searching with probabilities

Abstract : In this thesis we investigate two issues relating to heuristic search algorithms. The first and most important issue addressed is the technique used to represent knowledge within a search tree. Previous techniques have used either single values or ranges. We demonstrate that probability distributions, using a modified B*-type search algorithm, can successfully be used as a knowledge representation technique. Furthermore we show that the use of probability distributions is superior to the use of either of the previous techniques. The former conclusion is based on experiments that show that the probability-based algorithm is able to solve a wide variety of tactical chess problems. The latter conclusion is based on both analytical examples as well as experimental results. In analyzing search algorithms that use single-valued or range-based state descriptions, several important problems arise. For each problem we show how it is solved by the use of probability-based state descriptions. Experimentally we show that the probability-based algorithm solves over one-third more problems than the comparable range-based algorithm and expands approximately one-tenth the nodes on problems that both algorithms solve. Keywords: Reports, Military publications, Periodicals.