Creating AI Characters for Fighting Games using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Martinez-Arellano:2017:ieeeTCIAIG,

• author = "Giovanna Martinez-Arellano and Richard Cant and David Woods",
• title = "Creating AI Characters for Fighting Games using Genetic Programming",
• journal = "IEEE Transactions on Computational Intelligence and AI in Games",
• year = "2017",
• volume = "9",
• number = "4",
• pages = "423--434",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Adaptation models, Games, Learning (artificial intelligence), Real-time systems, AI, character, fighting games",
• ISSN = "1943-068X",
• DOI = "doi:10.1109/TCIAIG.2016.2642158",
• 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 game play 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.",
• notes = "Also known as \cite{7792145}",

}

Genetic Programming entries for Giovanna Martinez-Arellano Richard Cant David Woods

Citations