Case-based learning: Predictive features in indexing
dc.contributor.author | Seifert, Colleen M. | en_US |
dc.contributor.author | Hammond, Kristian J. | en_US |
dc.contributor.author | Johnson, Hollyn M. | en_US |
dc.contributor.author | Converse, Timothy M. | en_US |
dc.contributor.author | McDougal, Thomas F. | en_US |
dc.contributor.author | Vanderstoep, Scott W. | en_US |
dc.date.accessioned | 2006-09-11T18:23:19Z | |
dc.date.available | 2006-09-11T18:23:19Z | |
dc.date.issued | 1994-07 | en_US |
dc.identifier.citation | Seifert, Colleen M.; Hammond, Kristian J.; Johnson, Hollyn M.; Converse, Timothy M.; McDougal, Thomas F.; Vanderstoep, Scott W.; (1994). "Case-based learning: Predictive features in indexing." Machine Learning 16 (1-2): 37-56. <http://hdl.handle.net/2027.42/46928> | en_US |
dc.identifier.issn | 0885-6125 | en_US |
dc.identifier.issn | 1573-0565 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/46928 | |
dc.description.abstract | Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This “predictive features” hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate. | en_US |
dc.format.extent | 1360658 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Computer Science | en_US |
dc.subject.other | Computing Methodologies | en_US |
dc.subject.other | Artificial Intelligence (Incl. Robotics) | en_US |
dc.subject.other | Simulation and Modeling | en_US |
dc.subject.other | Language Translation and Linguistics | en_US |
dc.subject.other | Automation and Robotics | en_US |
dc.subject.other | Cased-based Reasoning | en_US |
dc.subject.other | Indexing | en_US |
dc.subject.other | Modeling | en_US |
dc.subject.other | Planning | en_US |
dc.subject.other | Analogical Reasoning | en_US |
dc.title | Case-based learning: Predictive features in indexing | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Science (General) | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Psychology, University of Michigan, 330 Packard Road, 48104, Ann Arbor, MI | en_US |
dc.contributor.affiliationum | Department of Psychology, University of Michigan, 330 Packard Road, 48104, Ann Arbor, MI | en_US |
dc.contributor.affiliationum | Department of Psychology, University of Michigan, 330 Packard Road, 48104, Ann Arbor, MI | en_US |
dc.contributor.affiliationother | Department of Computer Science, The University of Chicago, 1100 East 58th Street, 60637, Chicago, IL | en_US |
dc.contributor.affiliationother | Department of Computer Science, The University of Chicago, 1100 East 58th Street, 60637, Chicago, IL | en_US |
dc.contributor.affiliationother | Department of Computer Science, The University of Chicago, 1100 East 58th Street, 60637, Chicago, IL | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/46928/1/10994_2004_Article_BF00993173.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF00993173 | en_US |
dc.identifier.source | Machine Learning | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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