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   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">John Doyle.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">McGeachie, Michael J</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2005-05-19T14:59:38Z</dim:field>
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   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2002</dim:field>
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   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">51441242</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (p. 101-103).</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">This electronic version was submitted by the student author.  The certified thesis is available in the Institute Archives and Special Collections.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Ceteris paribus preference statements concisely represent preferences over outcomes or goals in a way natural to human thinking. Many decision making methods require an efficient method for comparing the desirability of two arbitrary goals. We address this need by presenting an algorithm for converting a set of qualitative ceteris paribus preferences into a quantitative utility function. Our algorithm is complete for a finite universe of binary features. Constructing the utility function can, in the worst case, take time exponential in the number of features. Common forms of independence conditions reduce the computational burden. We present heuristics using utility independence and constraint based search to achieve efficient utility functions.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Michael McGeachie.</dim:field>
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   <dim:field mdschema="dc" element="rights" lang="en_US">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.</dim:field>
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   <dim:field mdschema="dc" element="subject" lang="en_US">Electrical Engineering and Computer Science.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Utility functions for ceteris paribus preferences</dim:field>
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   	&lt;Title>Utility functions for ceteris paribus preferences&lt;/Title>
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   	&lt;Abstract>Ceteris paribus preference statements concisely represent preferences over outcomes or goals in a way natural to human thinking. Many decision making methods require an efficient method for comparing the desirability of two arbitrary goals. We address this need by presenting an algorithm for converting a set of qualitative ceteris paribus preferences into a quantitative utility function. Our algorithm is complete for a finite universe of binary features. Constructing the utility function can, in the worst case, take time exponential in the number of features. Common forms of independence conditions reduce the computational burden. We present heuristics using utility independence and constraint based search to achieve efficient utility functions.&lt;/Abstract>
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