Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/29478
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.contributor.author | Levine, John | - |
dc.contributor.author | Hallam, John | - |
dc.date.accessioned | 2018-04-23T12:34:57Z | - |
dc.date.available | 2018-04-23T12:34:57Z | - |
dc.date.issued | 2004-06 | - |
dc.identifier.citation | Yannakakis, G. N., Levine, J., & Hallam, J. (2004). An evolutionary approach for interactive computer games. Congress on Evolutionary Computation, 2004. CEC2004. Vol. 1. IEEE, Portland. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/29478 | - |
dc.description | The authors would like to thank Jeong Keun Park for his valuable contribution to the graphical representation of the Dead End game. | en_GB |
dc.description.abstract | In this paper we introduce the first stage of experiments on neuro-evolution mechanisms applied to predator/prey multi-character computer games. Our test-bed is a computer game where the prey (i.e. player) has to avoid its predators by escaping through an exit without getting killed. By viewing the game from the predators’ (i.e. opponents’) perspective, we attempt off-line to evolve neural-controlled opponents capable of playing effectively against computer-guided fixed strategy players. Their efficiency is based on cooperation which emerges from an abstract type of partial interaction with their environment. In addition, investigation of behavior generalization demonstrated the crucial contribution of playing strategies in the development of successful predator behaviors. However, emergent well-behaved opponents trained off-line with fixed strategies do not make the game interesting to play. We therefore present an evolutionary mechanism for opponents that keep learning from a player while playing against it (i.e. on-line) and we demonstrate its efficiency and robustness in increasing the predators’ performance while altering their behavior as long as the game is played. Computer game opponents following this on-line learning approach show high adaptability to changing player strategies, which provides evidence for the approach’s effectiveness and interest against human players. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Computer games | en_GB |
dc.subject | Human-computer interaction | en_GB |
dc.subject | Evolutionary computation | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.title | An evolutionary approach for interactive computer games | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | Congress on Evolutionary Computation 2004 | en_GB |
dc.bibliographicCitation.conferenceplace | Portland, OR, USA, 19-23/06/2004 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/CEC.2004.1330969 | - |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
An_evolutionary_approach_for_interactive_computer_games_2004.pdf | 390.85 kB | Adobe PDF | View/Open |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.