Generating Chess Moves using PVM

Game playing is one of the oldest areas of endeavors in artificial intelligence. A chess playing computer is an existence proof of a machine doing something thought to require intelligence. Chess programs have three major components: move generation, search and evaluation. The speed of a chess program depends on the quality of the mechanisms used to prune search of unprofitable continuations. The most reliable pruning method in popular use is the alpha-beta algorithm. This paper presents a parallel alpha-beta searching algorithm and examines the performance of its implementation. 1 Introduction The problem of playing chess can be defined as a problem of moving around in a state space where each state corresponds to a legal position of the board. The goal state is any board position in which the opponent does not have a legal move and his king is under attack. Chess has an average branching factor of about 35 and games often go to 50 moves by each player, so the search tree has about 35..