Abstract
Sequential decision tasks represent a difficult class of problem where perfect solutions are often not available in advance. This paper presents a set of experiments involving populations of agents that evolve to play games of tic–tac–toe. The focus of the paper is to propose that cultural learning, i.e. the passing of information from one generation to the next by non–genetic means, is a better approach than population learning alone, i.e. the purely genetic evolution of agents. Population learning is implemented using genetic algorithms that evolve agents containing a neural network capable of playing games of tic–tac–toe. Cultural learning is introduced by allowing highly fit agents to teach the population, thus improving performance. We show via experimentation that agents employing cultural learning are better suited to solving a sequential decision task (in this case tic–tac–toe) than systems using population learning alone.
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Curran, D., O’Riordan, C. (2004). Cultural Evolution for Sequential Decision Tasks: Evolving Tic–Tac–Toe Players in Multi–agent Systems. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_7
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DOI: https://doi.org/10.1007/978-3-540-24854-5_7
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