Multi-agent evolutionary systems for the generation of complex virtual worlds

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate i...

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Main Authors: J. Kruse, A. M. Connor
Format: Article
Language:English
Published: European Alliance for Innovation (EAI) 2016-01-01
Series:EAI Endorsed Transactions on Creative Technologies
Subjects:
Online Access:http://eudl.eu/doi/10.4108/eai.20-10-2015.150099
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spelling doaj-ae834731903a449c937790eec629dddc2020-11-24T21:47:55ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Creative Technologies2409-97082016-01-012511610.4108/eai.20-10-2015.150099Multi-agent evolutionary systems for the generation of complex virtual worldsJ. Kruse0A. M. Connor1Auckland University of Technology, Auckland, New ZealandAuckland University of Technology, Auckland, New Zealand; andrew.connor@aut.ac.nzModern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer’s intent through interaction, and encourages playful discovery.http://eudl.eu/doi/10.4108/eai.20-10-2015.150099evolutionary computationgenetic algorithmsautonomous agentsmulti-agent systemsinteractive design
collection DOAJ
language English
format Article
sources DOAJ
author J. Kruse
A. M. Connor
spellingShingle J. Kruse
A. M. Connor
Multi-agent evolutionary systems for the generation of complex virtual worlds
EAI Endorsed Transactions on Creative Technologies
evolutionary computation
genetic algorithms
autonomous agents
multi-agent systems
interactive design
author_facet J. Kruse
A. M. Connor
author_sort J. Kruse
title Multi-agent evolutionary systems for the generation of complex virtual worlds
title_short Multi-agent evolutionary systems for the generation of complex virtual worlds
title_full Multi-agent evolutionary systems for the generation of complex virtual worlds
title_fullStr Multi-agent evolutionary systems for the generation of complex virtual worlds
title_full_unstemmed Multi-agent evolutionary systems for the generation of complex virtual worlds
title_sort multi-agent evolutionary systems for the generation of complex virtual worlds
publisher European Alliance for Innovation (EAI)
series EAI Endorsed Transactions on Creative Technologies
issn 2409-9708
publishDate 2016-01-01
description Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer’s intent through interaction, and encourages playful discovery.
topic evolutionary computation
genetic algorithms
autonomous agents
multi-agent systems
interactive design
url http://eudl.eu/doi/10.4108/eai.20-10-2015.150099
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AT amconnor multiagentevolutionarysystemsforthegenerationofcomplexvirtualworlds
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