Abstract
We present an online, heuristic algorithm for learning Conditional Preference networks (CP-nets) from user queries. This is the first efficient and resolute CP-net learning algorithm: if a preference order can be represented as a CP-net, our algorithm learns a CP-net in time np, where p is a bound on the number of parents a node may have. The learned CP-net is guaranteed to be consistent with the original CP-net on all queries from the learning process. We tested the algorithm on randomly generated CP-nets; the learned CP-nets agree with the originals on a high percent of non-training preference comparisons.
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Guerin, J.T., Allen, T.E., Goldsmith, J. (2013). Learning CP-net Preferences Online from User Queries. In: Perny, P., Pirlot, M., Tsoukià s, A. (eds) Algorithmic Decision Theory. ADT 2013. Lecture Notes in Computer Science(), vol 8176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41575-3_16
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DOI: https://doi.org/10.1007/978-3-642-41575-3_16
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