Alpha-Beta Pruning for Games with Simultaneous Moves

Alpha-Beta pruning is one of the most powerful and fundamental MiniMax search improvements. It was designed for sequential two-player zero-sum perfect information games. In this paper we introduce an Alpha-Beta-like sound method more general class “stacked matrix games” that allow simultaneous moves by both players. This accomplished maintaining upper lower bounds achievable payoffs in states with actions dominated action based on feasibility certain linear programs. Empirical data shows considerable savings terms expanded nodes compared to naive depth-first move computation without pruning.