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Opportunism and Learning
dc.contributor.authorHammond, Kristian J.en_US
dc.contributor.authorSeifert, Colleen M.en_US
dc.date.accessioned2006-09-11T18:18:15Z
dc.date.available2006-09-11T18:18:15Z
dc.date.issued1993-03en_US
dc.identifier.citationHammond, Kristian; Seifert, Colleen M.; (1993). "Opportunism and Learning." Machine Learning 10(3): 279-309. <http://hdl.handle.net/2027.42/46870>en_US
dc.identifier.issn0885-6125en_US
dc.identifier.issn1573-0565en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/46870
dc.description.abstractThere is a tension in the world between complexity and simplicity. On one hand, we are faced with a richness of environment and experience that is at times overwhelming. On the other, we seem to be able to cope and even thrive within this complexity through the use of simple scripts, stereotypical judgements, and habitual behaviors. In order to function in the world, we have idealized and simplified it in a way that makes reasoning about it more tractable. As a group and as individuals, human agents search for and create islands of simplicity and stability within a sea of complexity and change.en_US
dc.format.extent2341161 bytes
dc.format.extent3115 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherKluwer Academic Publishers-Plenum Publishers; Kluwer Academic Publishers ; Springer Science+Business Mediaen_US
dc.subject.otherComputer Scienceen_US
dc.subject.otherComputer Science, Generalen_US
dc.subject.otherArtificial Intelligence (Incl. Robotics)en_US
dc.subject.otherAutomation and Roboticsen_US
dc.subject.otherCase-based Planningen_US
dc.subject.otherOpportunismen_US
dc.subject.otherStabilization of Environmentsen_US
dc.subject.otherControl of Executionen_US
dc.titleOpportunism and Learningen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelScience (General)en_US
dc.subject.hlbsecondlevelComputer Scienceen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Psychology, University of Michigan, 330 Packard Road, Ann Arbor, MI, 48104en_US
dc.contributor.affiliationotherDepartment of Computer Science, Artificial Intelligence Laboratory, The University of Chicago, 1100 East 58th Street, Chicago, IL, 60637en_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/46870/1/10994_2004_Article_422944.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1023/A:1022639127361en_US
dc.identifier.sourceMachine Learningen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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