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Game playing has always provided an exciting avenue of research in Artificial Intelligence. Various methodologies and techniques have been developed to build intelligent game players. Coevolution has proven to be successful in learning how to play games with no prior game knowledge. In this paper we develop a coevolutionary system for the General Game Playing framework, where absolutely nothing is...
General game playing has emerged, in recent years, as a challenging testbed for artificial intelligence research. The premise is to build game players that are able to play any game without prior knowledge about the game. The purpose of this paper is to give an overview of the open problems in the current form of general game playing, along with a short survey of work already done. We also provide...
In the recent years, wireless technology has enjoyed a tremendous rise in popularity and usage, thus opening new fields of applications in the domain of networking. One such field concerns mobile ad hoc networks (MANETs) where the participating nodes do not rely on any existing network infrastructure. By definition, the nature of ad hoc networks is dynamically changing and they have a fully decentralized...
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...
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