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dc.contributor.authorSalge, Christoph
dc.contributor.authorPolani, D.
dc.contributor.editorLenaerts, Tom
dc.contributor.editorGiacobini, Mario
dc.contributor.editorBersini, Hugues
dc.contributor.editorBourgine, Paul
dc.contributor.editorDorigo, Marco
dc.contributor.editorDoursat, René
dc.date.accessioned2012-03-06T10:00:08Z
dc.date.available2012-03-06T10:00:08Z
dc.date.issued2011
dc.identifier.citationSalge , C & Polani , D 2011 , Local Information Maximisation creates Emergent Flocking Behaviour . in T Lenaerts , M Giacobini , H Bersini , P Bourgine , M Dorigo & R Doursat (eds) , Advances in Artificial Life, ECAL 2011 : Proceedings of the Eleventh European Conference on the Synthesis and Simulation of Living Systems . MIT Press , Paris, France , pp. 688-696 . < http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12760 >
dc.identifier.isbn0-262-29714-0
dc.identifier.isbn978-0-262-29714-1
dc.identifier.otherBibtex: urn:cdf3622a8e11a0932303828e5b5cbb38
dc.identifier.otherORCID: /0000-0002-3233-5847/work/86098157
dc.identifier.urihttp://hdl.handle.net/2299/7903
dc.description.abstractThe three boids rules of alignment, separation and cohesion, introduced by Reynolds to recreate flocking behaviour have become a well known standard to create swarm behaviour. In this paper we want to demonstrate how similar flocking behaviour can be created by a local, agent based model, following a principle of information maximisation. The basis for our model is an extension of Vergassola’s infotaxis model, where agents determine their actions based on the highest expected reduction of entropy. We adapted this approach to a grid world-based search task, and extended the agents abilities so they could not only perform a Bayesian update with information gained from the environment, but also with information gained from other agents. The resulting global flocking behaviour is then analysed in regard to how well it resembles the basic boids rules.en
dc.format.extent9
dc.format.extent355559
dc.language.isoeng
dc.publisherMIT Press
dc.relation.ispartofAdvances in Artificial Life, ECAL 2011
dc.titleLocal Information Maximisation creates Emergent Flocking Behaviouren
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.identifier.urlhttp://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12760
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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