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dc.contributor.authorGaudl, SEen
dc.date.accessioned2019-10-30T14:18:23Z
dc.date.available2019-10-30T14:18:23Z
dc.identifier.urihttp://hdl.handle.net/10026.1/15094
dc.descriptionGenetic Programming, GP, Game AI, Agent Design, Platformer, AISB, JGAP, platformerAI, symbolic learningen
dc.description.abstract

There currently exists a wide range of techniques to model and evolve artificial players for games. Existing techniques range from black box neural networks to entirely hand-designed solutions. In this paper, we demonstrate the feasibility of a genetic programming framework using human controller input to derive meaningful artificial players which can, later on, be optimised by hand. The current state of the art in game character design relies heavily on human designers to manually create and edit scripts and rules for game characters. To address this manual editing bottleneck, current computational intelligence techniques approach the issue with fully autonomous character generators, replacing most of the design process using black box solutions such as neural networks or the like. Our GP approach to this problem creates character controllers which can be further authored and developed by a designer it also offers designers to included their play style without the need to use a programming language. This keeps the designer in the loop while reducing repetitive manual labour. Our system also provides insights into how players express themselves in games and into deriving appropriate models for representing those insights. We present our framework, supporting findings and open challenges.

en
dc.language.isoenen
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectcs.AIen
dc.subjectcs.AIen
dc.titleA Genetic Programming Framework for 2D Platform AIen
dc.typeConference Contribution
plymouth.author-urlhttp://arxiv.org/abs/1803.01648v1en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.rights.embargoperiodNot knownen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc/4.0/en
rioxxterms.typeConference Paper/Proceeding/Abstracten


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