Analysing the influence of the fitness function on genetically programmed bots for a real-time strategy game
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{FernandezAres:2017:EC,
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author = "A. Fernandez-Ares and A. M. Mora and
P. Garcia-Sanchez and P. A. Castillo and J. J. Merelo",
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title = "Analysing the influence of the fitness function on
genetically programmed bots for a real-time strategy
game",
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journal = "Entertainment Computing",
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volume = "18",
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pages = "15--29",
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year = "2017",
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ISSN = "1875-9521",
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DOI = "doi:10.1016/j.entcom.2016.08.001",
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URL = "http://www.sciencedirect.com/science/article/pii/S1875952116300222",
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abstract = "Finding the global best strategy for an autonomous
agent (bot) in a RTS game is a hard problem, mainly
because the techniques applied to do this must deal
with uncertainty and real-time planning in order to
control the game agents. This work describes an
approach applying a Genetic Programming (GP) algorithm
to create the behavioural engine of bots able to play a
simple RTS. Normally it is impossible to know in
advance what kind of strategies will be the best in the
most general case of this problem. So GP, which
searches the general decision tree space, has been
introduced and used successfully. However, it is not
straightforward what fitness function would be the most
convenient to guide the evolutionary process in order
to reach the best solutions and also being less
sensitive to the uncertainty present in the context of
games. Thus, in this paper three different evaluation
functions have been proposed, and a detailed analysis
of their performance has been conducted. The paper also
analyses several aspects of the obtained bots, in
addition to their final performance on battles, such as
the evolution of the decision trees (behavioural
models) themselves, or the influence on the results of
noise or uncertainty. The results show that a
victory-based fitness, which prioritises the number of
victories, contributes to generate better bots, on
average, than other functions based on other numerical
aspects of the battles, such as the number of resources
gathered, or the number of units generated.",
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keywords = "genetic algorithms, genetic programming, Real-time
strategy game, Autonomous agent, Bot, Fitness function,
Uncertainty",
- }
Genetic Programming entries for
Antonio Fernandez-Ares
Antonio M Mora Garcia
Pablo Garcia-Sanchez
Pedro A Castillo Valdivieso
Juan Julian Merelo
Citations