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Conference Paper: Opponent-based tactic selection for a first person shooter game
Title | Opponent-based tactic selection for a first person shooter game |
---|---|
Authors | |
Keywords | Adaptive Ai Machine Learning Opponent Modeling Quake 3 Student Modeling Video Games |
Issue Date | 2011 |
Citation | Icaart 2011 - Proceedings Of The 3Rd International Conference On Agents And Artificial Intelligence, 2011, v. 1, p. 591-594 How to Cite? |
Abstract | Video games are quickly becoming a significant part of society with a growing industry that employs a wide range of talent, from programmers to graphic artists. Video games are also becoming an interesting and useful testbed for Artificial Intelligence research. Complex, realistic environmental constraints, as well as performance considerations demand highly efficient AI techniques. At the same time, the AI component of a video game may define the ongoing commercial success, or failure, of a particular game or game engine. This research details an approach to opponent modeling in a first person shooter game, and evaluates proficiency gains facilitated by such a technique. Information about the user is recorded and used by the existing Artificial Intelligence component to select tactics for any given opponent. The evaluation results show that when computer characters use such modeling they are more effective than when they do not model their opponent. |
Persistent Identifier | http://hdl.handle.net/10722/179608 |
References |
DC Field | Value | Language |
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dc.contributor.author | Thomson, D | en_US |
dc.contributor.author | Mitrovic, A | en_US |
dc.date.accessioned | 2012-12-19T10:00:10Z | - |
dc.date.available | 2012-12-19T10:00:10Z | - |
dc.date.issued | 2011 | en_US |
dc.identifier.citation | Icaart 2011 - Proceedings Of The 3Rd International Conference On Agents And Artificial Intelligence, 2011, v. 1, p. 591-594 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/179608 | - |
dc.description.abstract | Video games are quickly becoming a significant part of society with a growing industry that employs a wide range of talent, from programmers to graphic artists. Video games are also becoming an interesting and useful testbed for Artificial Intelligence research. Complex, realistic environmental constraints, as well as performance considerations demand highly efficient AI techniques. At the same time, the AI component of a video game may define the ongoing commercial success, or failure, of a particular game or game engine. This research details an approach to opponent modeling in a first person shooter game, and evaluates proficiency gains facilitated by such a technique. Information about the user is recorded and used by the existing Artificial Intelligence component to select tactics for any given opponent. The evaluation results show that when computer characters use such modeling they are more effective than when they do not model their opponent. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence | en_US |
dc.subject | Adaptive Ai | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Opponent Modeling | en_US |
dc.subject | Quake 3 | en_US |
dc.subject | Student Modeling | en_US |
dc.subject | Video Games | en_US |
dc.title | Opponent-based tactic selection for a first person shooter game | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Thomson, D: dthomson@hku.hk | en_US |
dc.identifier.authority | Thomson, D=rp00788 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.scopus | eid_2-s2.0-79960146006 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-79960146006&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 1 | en_US |
dc.identifier.spage | 591 | en_US |
dc.identifier.epage | 594 | en_US |
dc.identifier.scopusauthorid | Thomson, D=7202586830 | en_US |
dc.identifier.scopusauthorid | Mitrovic, A=7003631144 | en_US |