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dc.contributor.authorFerguson, Garyen_US
dc.contributor.authorVidal, Francken_US
dc.contributor.editorRitsos, Panagiotis D. and Xu, Kaien_US
dc.date.accessioned2020-09-10T06:27:48Z
dc.date.available2020-09-10T06:27:48Z
dc.date.issued2020
dc.identifier.isbn978-3-03868-122-9
dc.identifier.urihttps://doi.org/10.2312/cgvc.20201148
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20201148
dc.description.abstractAs video games grow in complexity and require increasingly large and immersive environments, there is a need for more believable and dynamic characters not controlled by the player, known as non-player character (NPC). Video game developers will often face the challenge of designing these NPCs in a time efficient manner. We propose an agent-based Cooperative Co-evolution Algorithm (CCEA) where NPCs are implemented as artificial life (AL) agents that are created through an evolutionary process based on simple rules. The virtual environment can be filled with a range of interesting agents, each acting independently from one another, to fulfil their own wants and needs. The proposed middleware framework is suitable for computer animation of NPCs and the development of video games, especially where swarm intelligence is simulated. We proved that agents implemented with a very limited number of variables making up their genome can be successfully integrated in a co-evolutionary multi-agent system (CoEMAS). Results showed promising levels of speciation and interesting emergent and plausible behaviours amongst the agents.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectImage processing
dc.subjectArtificial life
dc.subjectAgent / discrete models
dc.subjectReal
dc.subjecttime simulation
dc.subjectApplied computing
dc.subjectComputer games
dc.subjectMathematics of computing
dc.subjectEvolutionary algorithms
dc.titleSimulating Dynamic Ecosystems with Co-Evolutionary Agentsen_US
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.description.sectionheadersVisualisation and Machine Learning
dc.identifier.doi10.2312/cgvc.20201148
dc.identifier.pages33-37


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