The topic of "preferences" has recently attracted considerable attention in Artificial Intelligence (AI) research, notably in fields such as autonomous agents, non-monotonic reasoning, constraint satisfaction, planning, and qualitative decision theory. Preferences provide a means for specifying desires in a declarative way, which is a point of critical importance for AI. Drawing on past research on knowledge representation and reasoning, AI offers qualitative and symbolic methods for treating preferences that can reasonably complement hitherto existing approaches from other fields, such as decision theory. Needless to say, however, the acquisition of preference information is not always an easy task. Therefore, not only are modeling languages and suitable representation formalisms needed, but also methods for the automatic learning, discovery, modeling, and adaptation of preferences.

It is hence hardly surprising that methods for learning and constructing preference models from explicit or implicit preference information and feedback are among the very recent research trends in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. In all these areas, considerable progress has been made on the representation and the automated learning of preference models. The goal of this Dagstuhl Seminar is to bring together international researchers in these areas, thereby stimulating the interaction between these fields with the goal of advancing the state-of-the-art in preference learning. Topics of interest to the seminar include, but are not limited to

• quantitative and qualitative approaches to modeling preference information;
• preference extraction, mining, and elicitation;
• methodological foundations of preference learning (learning to rank, ordered classification, active learning, learning monotone models, ...)
• inference and reasoning about preferences;
• mathematical methods for ranking;
• applications of preference learning (web search, information retrieval, electronic commerce, games, personalization, recommender systems, ...)

The workshop program will consist of presentations of ongoing work by the participants, and there will be ample time for formal and informal discussions. We anticipate several benefits of the seminar. First, we are of course interested in advancing the state-of-the-art in preference learning from a theoretical, methodological as well as application-oriented point of view. Apart from that, however, we also hope that the seminar will help to further consolidate this research field, which is still in an early stage of its development. Last but not least, our goal is to connect preference learning with closely related fields and research communities. Ideally, people from different communities will share their experiences and perspectives, and will familiarize themselves with each other's techniques and terminologies.

• Nir Ailon (Technion - Haifa, IL) [dblp]
• Fabio Aiolli (University of Padova, IT) [dblp]
• Antti Airola (University of Turku, FI) [dblp]
• Cedric Archambeau (Amazon - Berlin, DE) [dblp]
• Daniel Baier (BTU Cottbus, DE) [dblp]
• Jerzy Blaszczynski (Poznan University of Technology, PL) [dblp]
• Robert Busa-Fekete (Universität Marburg, DE) [dblp]
• Weiwei Cheng (Universität Marburg, DE) [dblp]
• Yann Chevaleyre (University of Paris North, FR) [dblp]
• Krzysztof Dembczynski (Poznan University of Technology, PL) [dblp]
• Sebastien Destercke (Technical University of Compiegne, FR) [dblp]
• Ad J. Feelders (Utrecht University, NL)
• Johannes Fürnkranz (TU Darmstadt, DE) [dblp]
• Andreas Geyer-Schulz (KIT - Karlsruher Institut für Technologie, DE) [dblp]
• Joachim Giesen (Universität Jena, DE) [dblp]
• Salvatore Greco (University of Portsmouth, GB) [dblp]
• Willem J. Heiser (Leiden University, NL) [dblp]
• Eyke Hüllermeier (Universität Marburg, DE) [dblp]
• Dietmar Jannach (TU Dortmund, DE) [dblp]
• Ulrich Junker (Biot, FR) [dblp]
• Kristian Kersting (TU Dortmund, DE) [dblp]
• Wojciech Kotlowski (Poznan University of Technology, PL) [dblp]
• Jérôme Lang (University Paris-Dauphine, FR) [dblp]
• Eneldo Loza Mencía (TU Darmstadt, DE) [dblp]
• Jerome Mengin (Paul Sabatier University - Toulouse, FR) [dblp]
• Vincent Mousseau (Ecole Centrale Paris, FR) [dblp]
• Ingrid Oliveira de Nunes (Federal University of Rio Grande do Sul, BR) [dblp]
• Alena Otto (Universität Siegen, DE) [dblp]
• Tapio Pahikkala (University of Turku, FI) [dblp]
• Marc Pirlot (University of Mons, BE) [dblp]
• Michael Rademaker (Ghent University, BE) [dblp]
• Francesca Rossi (University of Padova, IT) [dblp]
• Scott Sanner (NICTA - Canberra, AU) [dblp]
• Michele Sebag (University of Paris South XI, FR) [dblp]
• Eric Sibony (Télécom ParisTech, FR) [dblp]
• Roman Slowinski (Poznan University of Technology, PL) [dblp]
• Alexis Tsoukias (University Paris-Dauphine, FR) [dblp]
• Nicolas Usunier (Technical University of Compiegne, FR) [dblp]
• Kristen Brent Venable (Tulane University - New Orleans, US) [dblp]
• Paolo Viappiani (UPMC - Paris, FR) [dblp]
• Peter Vojtas (Charles University - Prague, CZ) [dblp]
• Toby Walsh (Data61 / NICTA - Sydney, AU) [dblp]
• Paul Weng (UPMC - Paris, FR) [dblp]
• Christian Wirth (TU Darmstadt, DE) [dblp]