General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and in the absence of game specific knowledge, become proficient players. Most GGP players have used standard tree-search techniques enhanced by automatic heuristic learning. In this paper we explore knowledge representation and learning in GGP using Reinforcement Learning and Ant Colony Algorithms. Knowledge is created by simulating random games. We test the quality of the knowledge by comparing the performance of players using the knowledge in a variety of games. The ideas presented in this paper provide the potential for a framework for learning and knowledge representation, given the total absence of any prior knowledge.
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