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Preferences in Artificial Intelligence

Pigozzi, Gabriella; Tsoukiàs, Alexis; Viappiani, Paolo (2016), Preferences in Artificial Intelligence, Annals of Mathematics and Artificial Intelligence, 77, 3, p. 361-401. 10.1007/s10472-015-9475-5

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Preferences in AI.pdf (510.0Kb)
Type
Article accepté pour publication ou publié
Date
2016
Journal name
Annals of Mathematics and Artificial Intelligence
Volume
77
Number
3
Pages
361-401
Publication identifier
10.1007/s10472-015-9475-5
Metadata
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Author(s)
Pigozzi, Gabriella
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Tsoukiàs, Alexis cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Viappiani, Paolo cc
Laboratoire d'Informatique de Paris 6 [LIP6]
Abstract (EN)
The paper presents a focused survey about the presence and the use of the concept of “preferences” in Artificial Intelligence. Preferences are a central concept for decision making and have extensively been studied in disciplines such as economy, operational research, decision analysis, psychology and philosophy. However, in the recent years it has also become an important topic both for research and applications in Computer Science and more specifically in Artificial Intelligence, in fields spanning from recommender systems to automatic planning, from non monotonic reasoning to computational social choice and algorithmic decision theory. The survey essentially covers the basics of preference modelling, the use of preference in reasoning and argumentation, the problem of compact representations of preferences, preference learning and the use of non conventional preference models based on extended logical languages. It aims at providing a general reference for all researchers both in Artificial Intelligence and Decision Analysis interested in this exciting interdisciplinary topic.
Subjects / Keywords
Preferences; Preference-based systems; Reasoning about preferences; Preferences in logic; Non-monotonic logic; Representation of preferences; Preference elicitation; Utility elicitation; Non classical preference models

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