An Emergence Of Game Strategy In Multiagent Systems

In this paper, we study an emergence of game strategy in multiagent systems. Symbolic and subsymbolic approaches are compared. Symbolic approach is represented by a backtrack algorithm with specified search depth, whereas the subsymbolic approach is represented by feedforward neural networks that are adapted by reinforcement temporal difference TD(lambda) technique. As a test game, we used simplified checkers. Three different strategies were used. The first strategy corresponds to a single agent that repeatedly plays games against MinMax version of a backtrack search method. The second strategy corresponds to single agents that are repeatedly playing a megatournament. The third strategy is an evolutionary modification of the second one. It is demonstrated that all these approaches led to a population of agents very successfully playing checkers against a backtrack algorithm with the search depth 3.