title = "Learning Environment Models in Car Racing Using
Stateful Genetic Programming",
booktitle = "Proceedings of the 2011 IEEE Conference on
Computational Intelligence and Games",
year = "2011",
address = "Seoul, South Korea",
pages = "219--226",
month = "31 " # aug # " - 3 " # sep,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, Reinforcement
Learning, Multiobjective Evolution, AI in Computer
Games, Car Racing, AI game agent, computational
intelligence, diverse opponent generation, game play
learning, nonplayer character, computer games,
evolutionary computation, learning (artificial
intelligence), multi-agent systems, 2D data structures,
artificial agents, car racing games, learning
environment models, model building behaviour, modular
programs, non player characters, cognition, computer
games, data structures, learning (artificial
intelligence), multi-agent systems",
abstract = "For computational intelligence to be useful in
creating game agent AI we need to focus on methods that
allow the creation and maintenance of models for the
environment, which the artificial agents inhabit.
Maintaining a model allows an agent to plan its actions
more effectively by combining immediate sensory
information along with a memories that have been
acquired while operating in that environment. To this
end, we propose a way to build environment models for
non-player characters in car racing games using
stateful Genetic Programming. A method is presented,
where general-purpose 2-dimensional data-structures are
used to build a model of the racing track. Results
demonstrate that model-building behaviour can be
cooperatively coevolved with car-controlling behaviour
in modular programs that make use of these models in
order to navigate successfully around a racing track.",