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dc.contributor.advisorLeslie Pack Kaelbling.en_US
dc.contributor.authorDeshpande, Ashwinen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2008-05-19T16:05:07Z
dc.date.available2008-05-19T16:05:07Z
dc.date.copyright2007en_US
dc.date.issued2007en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/41648
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.en_US
dc.descriptionIncludes bibliographical references (p. 57-58).en_US
dc.description.abstractWhile large data sets have enabled machine learning algorithms to act intelligently in complex domains, standard machine learning algorithms perform poorly in situations in which little data exists for the desired target task. Transfer learning attempts to extract trends from the data of similar source tasks to enhance learning in the target task. We apply transfer learning to probabilistic rule learning to learn the dynamics of a target world. We utilize a hierarchical Bayesian framework and specify a generative model which dictates the probabilities of task data, task rulesets and a common global ruleset. Through a greedy coordinated-ascent algorithm, the source tasks contribute towards building the global ruleset which can then be used as a prior to supplement the data from the target ruleset. Simulated experimental results in a variety of blocks-world domains suggest that employing transfer learning can provide significant accuracy gains over traditional single task rule learning algorithms.en_US
dc.description.statementofresponsibilityby Ashwin Deshpande.en_US
dc.format.extent58 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleLearning probabilistic relational dynamics for multiple tasksen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc219711442en_US


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