GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players

Hauptman, Ami; Sipper, Moshe

doi:10.1007/978-3-540-31989-4_11

Cited by 58 publications

(29 citation statements)

References 4 publications

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“…Genetic programming was successfully employed by Hauptman and Sipper [15,16] for evolving programs that can solve Mate-in-N problems and play chess endgames.…”

Section: Previous Evolutionary Methods Applied To Chessmentioning

confidence: 99%

Genetic algorithms for mentor-assisted evaluation function optimization

David-Tabibi

Koppel

Netanyahu

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate mentor, we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program with a smaller number of parameters in its evaluation function to mimic the behavior of a superior mentor which uses a more extensive evaluation function. In principle, our mentor-assisted approach could be used in a wide range of problems for which appropriate mentors are available.

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“…Genetic programming was successfully employed by Hauptman and Sipper [15,16] for evolving programs that can solve Mate-in-N problems and play chess endgames.…”

Section: Previous Evolutionary Methods Applied To Chessmentioning

confidence: 99%

Genetic algorithms for mentor-assisted evaluation function optimization

David-Tabibi

Koppel

Netanyahu

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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“…Genetic programming, a population-based evolutionary algorithm, has been successfully and widely applied to the creation of competitive teams for cooperative video games, such as soccer teams [23], [24] and for individual games such as backgammon [25] and chess [26]. To the best of our knowledge, GP has not been applied to the creation of AI characters for fighting games.…”

Section: Related Workmentioning

confidence: 99%

Creating AI Characters for Fighting Games Using Genetic Programming

Martínez-Arellano

Cant

Woods

2017

IEEE Trans. Comput. Intell. AI Games

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Abstract-This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a Genetic Programming algorithm that refines randomly generated character strategies into better ones using tournament selection.The generated AI characters were tested by twenty-seven human players and were rated according to results, perceived difficulty and how engaging the gameplay was. The main advantages of this procedure are that no prior knowledge of how to code the strategies of the AI character is needed and there is no need to interact with the internal code of the game. In addition, the procedure is capable of creating a wide diversity of players with different strategic skills, which could be potentially used as a starting point to a further adaptive process.

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“…The final agent was evaluated against a commercially available Chess program and unofficially achieved near expert status and an increase in rating of almost 200% over the unevolved agent. In (Hauptman and Sipper, 2005), the endgame of Chess was the focus, and the opening and midgame were ignored. For the endgame situations, the agents started out poorly, but within several hundred generations, were capable of playing a grandmaster level engine nearly to a draw.…”

Section: Games and Evolutionary Neural Networkmentioning

confidence: 99%

“…Rather than reduce the decision space, we use evolutionary algorithms (Samuel, 1959;Schaeffer et al, 1992;Thrun, 1995;Pollack and Blair, 1998;Barone and While, 1999;Kendall and Whitwell, 2001;Lubberts and Miikkulainen, 2001;Tesauro, 2002;Hauptman and Sipper, 2005;Beattie et al, 2007) to teach our agents a guided path to a good solution. Evolutionary algorithms mimic natural evolution, and reward good decisions while punishing less desirable ones.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Evolving No-Limit Texas Hold'em Poker Playing Agents

Nicolai

Hilderman

2010

Proceedings of the International Conference on Evolutionary Computation

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Abstract:Computers have difficulty learning how to play Texas Hold'em Poker. The game contains a high degree of stochasticity, hidden information, and opponents that are deliberately trying to mis-represent their current state. Poker has a much larger game space than classic parlour games such as Chess and Backgammon. Evolutionary methods have been shown to find relatively good results in large state spaces, and neural networks have been shown to be able to find solutions to non-linear search problems. In this paper, we present several algorithms for teaching agents how to play No-Limit Texas Hold'em Poker using a hybrid method known as evolving neural networks. Furthermore, we adapt heuristics such as halls of fame and co-evolution to be able to handle populations of Poker agents, which can sometimes contain several hundred opponents, instead of a single opponent. Our agents were evaluated against several benchmark agents. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., with no co-evolution) using a large hall of fame. These results demonstrate the effectiveness of our algorithms in creating competitive No-Limit Texas Hold'em Poker agents.

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GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players

Cited by 58 publications

References 4 publications

Genetic algorithms for mentor-assisted evaluation function optimization

Genetic algorithms for mentor-assisted evaluation function optimization

Creating AI Characters for Fighting Games Using Genetic Programming

Algorithms for Evolving No-Limit Texas Hold'em Poker Playing Agents

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