dc.contributor.author |
Watson, Ian |
en |
dc.contributor.author |
Wender, Stefan |
en |
dc.coverage.spatial |
Perth, Australia |
en |
dc.date.accessioned |
2012-04-10T23:00:42Z |
en |
dc.date.accessioned |
2012-04-10T23:02:03Z |
en |
dc.date.issued |
2008 |
en |
dc.identifier.citation |
IEEE Symposium on Computational Intelligence and Games, Univ Western Australia, Perth, AUSTRALIA, 15 Dec 2008 - 18 Dec 2008. 2008 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND GAMES. IEEE. 372-377. 01 Jan 2008 |
en |
dc.identifier.isbn |
978-1-4244-2974-5 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/16926 |
en |
dc.description.abstract |
This paper describes the design and implementation of a reinforcement learner based on Q-Learning. This adaptive agent is applied to the city placement selection task in the commercial computer game Civilization IV. The city placement selection determines the founding sites for the cities in this turn-based empire building game from the Civilization series. Our aim is the creation of an adaptive machine learning approach for a task which is originally performed by a complex deterministic script. This machine learning approach results in a more challenging and dynamic computer AI. We present the preliminary findings on the performance of our reinforcement learning approach and we make a comparison between the performance of the adaptive agent and the original static game AI. Both the comparison and the performance measurements show encouraging results. Furthermore the behaviour and performance of the learning algorithm are elaborated and ways of extending our work are discussed. |
en |
dc.publisher |
IEEE |
en |
dc.relation.ispartof |
CIG'08, IEEE Symposium on Computational Intelligence and Games |
en |
dc.relation.ispartofseries |
IEEE Symposium on Computational Intelligence and Games |
en |
dc.relation.replaces |
http://hdl.handle.net/2292/16924 |
en |
dc.relation.replaces |
2292/16924 |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
Using Reinforcement Learning for City Site Selection in the Turn-Based Strategy Game Civilization IV |
en |
dc.type |
Conference Item |
en |
dc.identifier.doi |
10.1109/CIG.2008.5035664 |
en |
pubs.begin-page |
372 |
en |
dc.rights.holder |
Copyright: IEEE |
en |
pubs.end-page |
377 |
en |
pubs.finish-date |
2008-12-18 |
en |
pubs.start-date |
2008-12-15 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
81867 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
School of Computer Science |
en |
pubs.record-created-at-source-date |
2010-09-01 |
en |