Human-like intuitive playing in board games

Abstract

The paper provides an argumentation for potential virtues of developing cognitively-plausible human-like playing systems, thus advocates a return to the roots of Artificial Intelligence application to games. Such systems are, in particular, expected to be capable of intuitive playing, manifested by efficient search-free move pre-selection and application of shallow-search only during regular move analysis. The main facets of such systems are listed and discussed in the paper. Furthermore, an example of search-free playing system, in the form of a specifically-designed convoluted neural network, is presented to illustrate possible implementation of proposed ideas. © 2012 Springer-Verlag.