Abstract
Users’ preferences show up in different formats: quantitative vs. qualitative, conditional vs. unconditional, prioritized vs. flat, etc. In fact, depending on the reason, it could be more natural to express preferences in one a format or another. Thus, it became a challenge for researchers to adequately capture many varieties of preference forms. To this end, artificial intelligence has seen a number of compact languages for preference representation (cf. Chapter 3). However, each language has not been conceived to explicitly and naturally capture all forms of preferences. Instead, it focuses on a particular specification of preferences. It induces a preference relation over the set of outcomes which is a partial or total (pre)order.
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Kaci, S. (2011). Making Hidden Priorities Explicit. In: Working with Preferences: Less Is More. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17280-9_4
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DOI: https://doi.org/10.1007/978-3-642-17280-9_4
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