March 2 – 7 , 2014, Dagstuhl Seminar 14101
Scott Sanner (NICTA – Canberra, AU)
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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 was 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
- 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 main goal of the seminar was to advance 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 helped to further consolidate this research field, which is still in an early stage of its development. Last but not least, our goal was to connect preference learning with closely related fields and research communities.
In order to achieve these goals, the program featured the following components:
- Monday was filled with 6 tutorial-type introductory talks about the use of preferences and the view on preference learning in the areas of machine learning, recommender systems, multi-criteria decision making, business and economics, artificial intelligence, and social choice, with the goal of familiarizing the members of the different communities with the basics of the other fields.
- Ten sessions were devoted to contributed presentations, each one with enough extra time for discussion. In case we ran over time, we gave priority to discussions. We were also able to flexibly integrate a few impromptu talks by participants.
- Two discussion sessions on Tuesday and Thursday afternoon were devoted to discussion how to establish closer connections between the different research areas that participated in this seminar.
- Wednesday afternoon featured a hike and an excursion to Trier with some wine tasting.
Creative Commons BY 3.0 Unported license
Johannes Fürnkranz and Eyke Hüllermeier and Cynthia Rudin and Roman Slowinski and Scott Sanner
- Artificial Intelligence / Robotics
- Data Bases / Information Retrieval
- Machine learning
- Preference learning
- Preference elicitation
- Social choice
- Multiple criteria decision making
- Decision under risk and uncertainty
- User modeling
- Recommender systems
- Information retrieval