Computational Intelligence in Mind Games

This chapter discusses recent achievements and possible perspectives of Computational Intelligence (CI) applied to mind games. Several noticeable examples of unguided, autonomous learning systems based on CI are presented and their properties analyzed. Considering the advantages and limitations of existing approaches a list of challenging issues and open problems in the area of intelligent game playing is proposed and motivated. It is generally concluded in the paper that the ultimate goal of CI in mind game research is the ability to mimic human approach to game playing in all its major aspects including learning methods (learning from scratch, multitask learning, unsupervised learning, pattern-based knowledge acquisition) as well as reasoning and decision making (efficient position estimation, abstraction and generalization of game features, autonomous development of evaluation function, effective pre-ordering of moves and selective, contextual search).