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dc.contributor.authorBalch, Tucker
dc.contributor.authorDellaert, Frank
dc.contributor.authorFeldman, Adam
dc.contributor.authorGuillory, Andrew
dc.contributor.authorIsbell, Charles
dc.contributor.authorKhan, Zia
dc.contributor.authorPratt, Stephen
dc.contributor.authorStein, Andrew
dc.contributor.authorWilde, Hank
dc.date.accessioned2011-04-22T16:18:46Z
dc.date.available2011-04-22T16:18:46Z
dc.date.issued2006-07
dc.identifier.citationBalch, T., Dellaert, F., Feldman, A., Guillory, A., Isbell, C.L., Khan, Z., Stein, A.N., & Wilde, H. (2006). "How Multirobot Systems Research will Accelerate our Understanding of Social Animal Behavior”.en_US
dc.identifier.issn0018-9219
dc.identifier.urihttp://hdl.handle.net/1853/38691
dc.description©2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.description.abstractOur understanding of social insect behavior has significantly influenced A.I. and multi-robot systems’ research (e.g. ant algorithms and swarm robotics). In this work, however, we focus on the opposite question, namely: “how can multi-robot systems research contribute to the understanding of social animal behavior?.” As we show, we are able to contribute at several levels: First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multi-robot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectMulti-robot systemsen_US
dc.subjectSocial animalsen_US
dc.titleHow A.I. and multi-robot systems research will accelerate our understanding of social animal behavioren_US
dc.typePost-printen_US
dc.typeProceedings
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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