Well-known algorithms for the evaluation of the minimax function in game trees are alpha-beta [Knuth] and SSS* [Stockman]. An improved version of SSS* is SSS-2 [Pijls-1]. All these algorithms don't use any heuristic information on the game tree. In this paper the use of heuristic information is introduced into the alpha-beta and the SSS-2 algorithm. Extended versions of these algorithms are presented. The subset of nodes which is visited during execution of each algorithm is characterised completely. 1 Introduction In this paper several methods are discussed to compute the minimax function on a game tree with heuristic information. Game trees are related to two person games with perfect information like Chess, Checkers, Go, Tic-tac-toe, etc. Each node in a game tree represents a game position. The root represents a position of the game for which we want to find the best move. The children of each node n correspond to the positions resulting from one move from the position give..
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