Paper Info
Title
Genetic evolution of fuzzy finite state machines to control bots in a first-person shooter game
Abstract
In this work we employ a steady state genetic algorithm to evolve bots' behaviors in the Unreal Tournament 2004 game. Our aim is to show whether interesting behaviors can be obtained with simple fitness functions. For this purpose we define four functions, measuring the number of enemies killed, the bot's lifespan, a combination of both and the number of items collected. The experiments show that incorporating a measure of the bot's lifespan in the fitness results in an optimal behavior in all aspects considered; further, the bots evolved this way outperform the standard bots supplied by the game. In addition, there is an increase in the number of items collected (even when this is not explicitly included in the fitness) and a tendency towards a more optimised combat style with less aggressive behaviors.
Year
DOI
Venue
2010
10.1145/1830483.1830633
GECCO
Keywords
Field
DocType
interesting behavior,standard bots,optimised combat style,genetic algorithm,genetic evolution,fuzzy finite state machine,steady state,aggressive behavior,first-person shooter game,simple fitness function,fitness result,optimal behavior,unreal tournament,fitness function,genetics,genetic algorithms,autonomous agent,finite state machine,autonomous agents
Steady state genetic algorithm,Tournament,Autonomous agent,Genetic Evolution,Computer science,Fuzzy logic,Finite-state machine,Artificial intelligence,Genetic algorithm,Machine learning
Conference
Citations 
PageRank 
References 
3
0.39
3
Authors
5
Name
Order
Citations
PageRank
Anna I. Esparcia-Alcazár1745.98
Anais Martínez-García230.39
Antonio Miguel Mora331442.81
J. J. Merelo436333.51
Pablo García-sánchez518232.32