Achieving Efficient and Cognitively Plausible Learning in Backgammon

Traditionally, computer applications to game domains have taken a brute-force approach, relying on sheer computational power to overcome the complexity of the domain. Although many of these programs have been quite successful, it is interesting to note that humans can still perform extremely well against them. Thus we are compelled to ask, if no human could match the computational power of most of these programs, are there methods for learning and performance in game domains that more closely reflect human cognition? In response to this question, this paper attempts to model how humans learn and play games by developing a Backgammon-playing algorithm based on cognition. Analysis of this algorithm shows that it is efficient and commensurate with human abilities suggesting that it provides a cognitively plausible theory of learning in Backgammon.