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Learning environment models in car racing using stateful genetic programming
Date Issued
2011-08-31
Date Available
2012-04-17T13:57:05Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords
Subject – LCSH
Video games
Genetic programming (Computer science)
Machine learning
Web versions
Language
English
Status of Item
Peer reviewed
Part of
2011 IEEE Conference on Computational Intelligence and Games [proceedings]
Conference Details
Paper presented at the 2011 IEEE Conference on Computational Intelligence and Games (CIG’11), Seoul, South Korea, Aug.31-Sept.3, 2011
ISBN
978-1-4577-0009-5
This item is made available under a Creative Commons License
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