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Solving large stochastic planning problems using multiple dynamic abstractions

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dc.contributor.advisor Leslie Pack Kaelbling. en_US
dc.contributor.author Steinkraus, Kurt Alan, 1978- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2006-08-25T18:57:34Z
dc.date.available 2006-08-25T18:57:34Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/33928
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. en_US
dc.description Includes bibliographical references (p. 165-172). en_US
dc.description.abstract One of the goals of AI is to produce a computer system that can plan and act intelligently in the real world. It is difficult to do so, in part because real-world domains are very large. Existing research generally deals with the large domain size using a static representation and exploiting a single type of domain structure. This leads either to an inability to complete planning on larger domains or to poor solution quality because pertinent information is discarded. This thesis creates a framework that encapsulates existing and new abstraction and approximation methods into modules and combines arbitrary modules into a 'hierarchy that allows for dynamic representation changes. The combination of different abstraction methods allows many qualitatively different types of structure in the domain to be exploited simultaneously. The ability to change the representation dynamically allows the framework to take advantage of how different domain subparts are relevant in different ways at different times. Since the current plan tracks the current representation, choosing to simplify (or omit) distant or improbable areas of the domain sacrifices little in the way of solution quality while making the planning problem considerably easier. en_US
dc.description.abstract (cont.) The module hierarchy approach leads to greater abstraction that is tailored to the domain and therefore need not give up hope of creating reasonable solutions. While there are no optimality guarantees, experimental results show that suitable module choices gain computational tractability at little cost to behavioral optimality and allow the module hierarchy to solve larger and more interesting domains than previously possible. en_US
dc.description.statementofresponsibility by Kurt Alan Steinkraus. en_US
dc.format.extent 172 p. en_US
dc.format.extent 9775117 bytes
dc.format.extent 9782337 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.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.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Solving large stochastic planning problems using multiple dynamic abstractions en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 67299107 en_US


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