Decision Making in Unclear Situations

Abstract

Large domains often contain problem states which cannot be evaluated accurately enough (neither statically nor dynamically). However, it is necessary to make decisions in such an environment, too. Here we discuss some of the usual methods of decision making in two-person games and give an analysis of the problems induced by unclear situations. Two classes of such situations are distinguished and a model for improved decision making based on the point-valued minimax approach (for reasons of practicability) is presented. Asymmetric evaluations (depending on the side to move) help to avoid unclear situations the program does not understand. Moreover, the inclusion of the global state of the game helps to avoid risks in favourable situations and to play for counter-chances in disadvantageous ones. Practical results have been gained from the implementation in a chess program, showing that problems can be solved this way, which have been intractable up to now.