An Automatically Generated Evaluation Function in General Game Playing

General game-playing (GGP) competitions provide a framework for building multigame-playing agents. In this paper, we describe an attempt at the implementation of such an agent. It relies heavily on our knowledge-free method of automatic construction of an approximate state evaluation function, based on game rules only. This function is then employed by one of the two game tree search methods: MTD (f) or guided upper confidence bounds applied to trees (GUCT), the latter being our proposal of an algorithm combining UCT with the usage of an evaluation function. The performance of our agent is very satisfactory when compared to a baseline UCT implementation.