On imitating Connect-4 game trajectories using an approximate n-tuple evaluation function

Abstract

The effect of game trajectories on learning after-state evaluation functions for the game Connect-4 is investigated. The evaluation function is approximated using a linear function of n-tuple features. The learning is supervised by an AI game engine, called Velena, within a preference learning framework. A different distribution of game trajectories will be generated when applying the learned approximated evaluation function, which may degrade the performance of the player. A technique known as the Dagger method will be used to address this problem. Furthermore, the opponent playing strategy is a source for new game trajectories. Random play will be introduced to the game to model this behaviour. The method of introducing random play to the game will again form different game trajectories and result in various strengths of play learned. An empirical study of a number of techniques for the generation of game trajectories is presented and evaluated.