Abstract
Existing 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 grow exponentially in the number of attributes describing the outcomes. In this paper, a new approach for constructing COP-nets, using A* search, is introduced. Using this approach, partial COP-nets can be constructed on demand instead 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.
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© 2011 Her Majesty the Queen in Right of Canada
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Bediako-Asare, H., Buffett, S., Fleming, M.W. (2011). Utility Estimation in Large Preference Graphs Using A* Search. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_6
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DOI: https://doi.org/10.1007/978-3-642-21043-3_6
Publisher Name: Springer, Berlin, Heidelberg
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