Neuroevolution of content layout in the PCG: Angry bots video game

Raffe, WL; Zambetta, F; Li, X

Neuroevolution of content layout in the PCG: Angry bots video game

Raffe, WL

Zambetta, F Li, X

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE Congress on Evolutionary Computation, CEC 2013, 2013, pp. 673 - 680
Issue Date:: 2013-08-21

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Field	Value	Language
dc.contributor.author	Raffe, WL https://orcid.org/0000-0001-5310-0943	en_US
dc.contributor.author	Zambetta, F	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2013-08-21	en_US
dc.identifier.citation	2013 IEEE Congress on Evolutionary Computation, CEC 2013, 2013, pp. 673 - 680	en_US
dc.identifier.isbn	9781479904549	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120948
dc.description.abstract	This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE Congress on Evolutionary Computation, CEC 2013	en_US
dc.relation.isbasedon	10.1109/CEC.2013.6557633	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Neuroevolution of content layout in the PCG: Angry bots video game	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players. © 2013 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/120948