Show simple item record

dc.contributor.advisorHorsch, Michael C.en_US
dc.creatorLong, Jeffrey Richarden_US
dc.date.accessioned2006-08-28T14:41:30Zen_US
dc.date.accessioned2013-01-04T04:55:25Z
dc.date.available2006-08-29T08:00:00Zen_US
dc.date.available2013-01-04T04:55:25Z
dc.date.created2006-08en_US
dc.date.issued2006-08-21en_US
dc.date.submittedAugust 2006en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-08282006-144130en_US
dc.description.abstractGame balance is the problem of determining the fairness of actions or sets of actions in competitive, multiplayer games. This problem primarily arises in the context of designing board and video games. Traditionally, balance has been achieved through large amounts of play-testing and trial-and-error on the part of the designers. In this thesis, it is our intent to lay down the beginnings of a framework for a formal and analytical solution to this problem, combining techniques from game theory and machine learning. We first develop a set of game-theoretic definitions for different forms of balance, and then introduce the concept of a strategic abstraction. We show how machine classification techniques can be used to identify high-level player strategy in games, using the two principal methods of sequence alignment and Naive Bayes classification. Bioinformatics sequence alignment, when combined with a 3-nearest neighbor classification approach, can, with only 3 exemplars of each strategy, correctly identify the strategy used in 55\% of cases using all data, and 77\% of cases on data that experts indicated actually had a strategic class. Naive Bayes classification achieves similar results, with 65\% accuracy on all data and 75\% accuracy on data rated to have an actual class. We then show how these game theoretic and machine learning techniques can be combined to automatically build matrices that can be used to analyze game balance properties.en_US
dc.language.isoen_USen_US
dc.subjectgamesen_US
dc.subjectgame balanceen_US
dc.subjectmachine learningen_US
dc.subjectsequence alignmenten_US
dc.subjectgame theoryen_US
dc.subjectnaive bayesen_US
dc.titleGame theoretic and machine learning techniques for balancing gamesen_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record