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Adversarial constraint satisfaction by game-tree search

  • University College Cork

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

Many decision problems can be modelled as adversarial constraint satisfaction, which allows us to integrate methods from AI game playing. In particular, by using the idea of opponents, we can model both collaborative problem solving, where intelligent participants with different agendas must work together to solve a problem, and multi-criteria optimisation, where one decision maker must balance different objectives. In this paper, we focus on the case where two opponents take turns to instantiate constrained variables, each trying to direct the solution towards their own objective. We represent the process as game-tree search. We develop variable and value ordering heuristics based on game playing strategies.We examine the performance of various algorithms on general-sum graph colouring games, for both multi-participant and multi-criteria optimisation.

Original languageEnglish
Title of host publicationECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings
EditorsRamon Lopez de Mantaras, Lorenza Saitta
PublisherIOS Press BV
Pages151-155
Number of pages5
ISBN (Electronic)9781586034528
Publication statusPublished - 2004
Event16th European Conference on Artificial Intelligence, ECAI 2004 - Valencia, Spain
Duration: 22 Aug 200427 Aug 2004

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume110
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference16th European Conference on Artificial Intelligence, ECAI 2004
Country/TerritorySpain
CityValencia
Period22/08/0427/08/04

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