Designing competitive bots for a real time strategy game using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{DBLP:conf/cosecivi/Fernandez-AresGMCG14,
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author = "Antonio Fernandez-Ares and Pablo Garcia-Sanchez and
Antonio Miguel Mora and Pedro A. Castillo and
Juan Julian Merelo Guervos",
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title = "Designing competitive bots for a real time strategy
game using genetic programming",
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booktitle = "Proceedings 1st Congreso de la Sociedad Espanola para
las Ciencias del Videojuego, CoSECivi 2014",
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year = "2014",
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editor = "David Camacho and Marco Antonio Gomez-Martin and
Pedro Antonio Gonzalez-Calero",
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series = "CEUR Workshop Proceedings",
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volume = "1196",
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pages = "159--172",
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address = "Barcelona, Spain",
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month = jun # " 24",
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publisher = "CEUR-WS.org",
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keywords = "genetic algorithms, genetic programming",
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bibsource = "dblp computer science bibliography, http://dblp.org",
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URL = "http://ceur-ws.org/Vol-1196",
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URL = "http://ceur-ws.org/Vol-1196/cosecivi14_submission_24.pdf",
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size = "14 pages",
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abstract = "The design of the Artificial Intelligence (AI) engine
for an autonomous agent (bot) in a game is always a
difficult task mainly done by an expert human player,
who has to transform his/her knowledge into a
behavioural engine. This paper presents an approach for
conducting this task by means of Genetic Programming
(GP) application. This algorithm is applied to design
decision trees to be used as bot's AI in 1 vs 1 battles
inside the RTS game Planet Wars. Using this method it
is possible to create rule-based systems defining
decisions and actions, in an automatic way, completely
different from a human designer doing them from
scratch. These rules will be optimised along the
algorithm run, considering the bots' performance during
evaluation matches. As GP can generate and evolve
behavioural rules not taken into account by an expert,
the obtained bots could perform better than
human-defined ones. Due to the difficulties when
applying Computational Intelligence techniques in the
videogames scope, such as noise factor in the
evaluation functions, three different fitness
approaches have been implemented and tested in this
work. Two of them try to minimise this factor by
considering additional dynamic information about the
evaluation matches, rather than just the final result
(the winner), as the other function does. In order to
prove them, the best obtained agents have been compared
with a previous bot, created by an expert player (from
scratch) and then optimised by means of Genetic
Algorithms. The experiments show that the three used
fitness functions generate bots that outperform the
optimised human-defined one, being the area-based
fitness function the one that produces better
results.",
- }
Genetic Programming entries for
Antonio Fernandez-Ares
Pablo Garcia-Sanchez
Antonio M Mora Garcia
Pedro A Castillo Valdivieso
Juan Julian Merelo
Citations