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  1. ; ; (, Proceedings of the ... International Florida Artificial Intelligence Research Society Conference)
    Conditional preference networks (CP-nets) are an intuitive and expressive representation for qualitative preferences. Such models must somehow be acquired. Psychologists argue that direct elicitation is suspect. On the other hand, learning general CP-nets from pairwise comparisons is NP-hard, and --- for some notions of learning --- this extends even to the simplest forms of CP-nets. We introduce a novel, concise encoding of binary-valued, tree-structured CP-nets that supports the first local-search-based CP-net learning algorithms. While exact learning of binary-valued, tree-structured CP-nets --- for a strict, entailment-based notion of learning --- is already in P, our algorithm is the first space-efficient learning algorithm that gracefully handles noisy (i.e., realistic) comparison sets. 
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