Evolving and discovering tetris gameplay strategies

Abstract

This work is motivated by one of the important characteristics of an intelligent system: the ability to automatically discover new knowledge. This work employs an evolutionary technique to search for good solutions and then employs a data mining technique to extract knowledge implicitly encoded in the evolved solutions. In this paper, Genetic Algorithm (GA) is employed to evolve a solution for randomly generated tetromino sequences. In contrast to previous works in this area where an evolutionary strategy was employed to evolve weights (i.e., preferences) of predefined evaluation functions which were then used to determine players' actions, we directly evolve the gameplay actions. Each chromosome represents a plausible gameplay strategy and its fitness is evaluated by simulating the actual gameplay using gameplay instructions from each chromosome. In each simulation, 13 attributes relevant to the gameplay, i.e., contour patterns and actions of each tetromino, are recorded from the best evolved games. This produces 6583 instances which we then apply Apriori algorithm to extract association patterns from them. The result illustrates that sensible gameplay strategies can be successfully extracted from evolved games even though the GA was not informed about these gameplay strategies. 1877-0509