Utility Estimation in Large Preference Graphs Using A* Search

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DOIResolve DOI: http://doi.org/10.1007/978-3-642-21043-3-6
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ConferenceThe 24th Canadian Conference on Artificial Intelligence (AI 2011), May 25-27, 2011, St. John’s, Newfoundland and Labrador
AbstractExisting preference prediction techniques can require that an entire preference structure be constructed for a user. These structures, such as Conditional Outcome Preference Networks (COP-nets), can however grow exponentially in the number of attributes that describe the outcomes. In this paper, a different approach for constructing COP-nets, using A* search, is introduced. Using this new approach, partial COP-nets can be constructed dynamically or on demand as opposed to the current process of generating the entire structure. Experimental results show that the new method yields enormous savings in time and memory requirements, with only a modest reduction in prediction accuracy. One such large example shows only a 5% decrease in the suc- cess rate, while reducing computation time from over 3 hours to just 2 seconds.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number18024757
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Record identifier6fd74685-5084-4263-be3e-6b38afe3a70e
Record created2011-06-04
Record modified2016-05-09
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