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OFAI-TR-2003-26 ( 785kB PDF file)

Preference Learning: Models, Methods, Applications - Proceedings of the KI-2003 Workshop

Eyke Hüllermeier, Johannes Fürnkranz

The preferences of an individual, say, the participant of an electronic auction or the customer of an electronic store, can be expressed in various ways, either explicitly, e.g. in the form of preference statements or implicitly, e.g. through the way of acting in different situations. The problem of finding out about an individual's preferences, or about those of a group of individuals, is referred to as preference elicitation. This requires, among other things, formal models for representing preferences and methods for their (automatic) acquisition. Touching on various aspects of AI, both theoretical and practical, preference elicitation is one of this field's most recent and interesting research topics. This workshop, held at the German Conference for Artificial Intelligence (KI-2003), focused on learning methods for preference elicitation, that is on methods for inducing preferences from given observations. Like other types of complex learning tasks that have recently entered the stage in machine learning and related fields, preference learning deviates strongly from the standard problems of classification and regression. It is particularly challenging as it involves the prediction of complex structures, such as weak or partial order relations, rather than single values. Moreover, training input will not, as is usually the case, be offered in the form of complete examples but may comprise more general types of information, such as relative preferences or different kinds of indirect feedback.

Citation: Hüllermeier E., Fürnkranz J.: Preference Learning: Models, Methods, Applications - Proceedings of the KI-2003 Workshop. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2003-26, 2003