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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European Alliance for Innovation (EAI)
2016-01-01
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Online Access: | http://eudl.eu/doi/10.4108/eai.20-10-2015.150099 |
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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 |
work_keys_str_mv |
AT jkruse multiagentevolutionarysystemsforthegenerationofcomplexvirtualworlds AT amconnor multiagentevolutionarysystemsforthegenerationofcomplexvirtualworlds |
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1725894523672854528 |