Creating AI Characters for Fighting Games using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Martinez-Arellano:2017:ieeeTCIAIG,
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author = "Giovanna Martinez-Arellano and Richard Cant and
David Woods",
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title = "Creating AI Characters for Fighting Games using
Genetic Programming",
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journal = "IEEE Transactions on Computational Intelligence and AI
in Games",
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year = "2017",
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volume = "9",
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number = "4",
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pages = "423--434",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Adaptation
models, Games, Learning (artificial intelligence),
Real-time systems, AI, character, fighting games",
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ISSN = "1943-068X",
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DOI = "doi:10.1109/TCIAIG.2016.2642158",
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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 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