Some thoughts on using Computational Intelligence methods in classical mind board games

In the last two decades the advancement of AI/CI methods in classical board and card games (such as Chess, Checkers, Othello, Go, Poker, Bridge, …) has been enormous. In nearly all “world famous” board games humans have been decisively conquered by machines (actually Go remains almost the last redoubt of human supremacy). In the above perspective the natural question is whether there is still any need for further development of CI methods in this area. What kind of goals can be achieved on this path? What are (if any) the challenging problems in the field? The paper tries to discuss these issues with respect to classical board mind games and provides (highly subjective) partial answers to some of the open questions. The main conclusion from the arguments specified in the paper is that one of the major, ultimate goals of CI in classical board game research concerns possessing by machines the ability to mimic human approach to game playing. This includes human-specific learning methods (learning from scratch, pattern-based learning, multitask and unsupervised learning) and human-type reasoning and decision making (efficient position estimation, abstraction and generalization of game features, autonomous development of evaluation functions, effective pre-ordering of moves, and selective, contextual search). Three of the above listed issues i.e. autonomous learning, knowledge discovery and intuition are discussed in this paper in more detail.