Preference Handling for Artificial Intelligence
Papers from the AAAI Workshop
Jon Doyle, Judy Goldsmith, Ulrich Junker, and Jérôme Lang, Cochairs
Preferences guide human decision making from early childhood (for example, “which ice cream flavor do you prefer?”) to complex professional and organizational decisions (for example, “which investment funds to choose?”). Preferences are essential for making intelligent choices in complex situations, for mastering large sets of alternatives, and for coordinating a multitude of decisions. Explicit preference models allow an agent to reason about its own and the other agent’s behavior and to analyze and revise this behavior. AI tasks often need new forms of preference handling beyond classic utility-based models. Recent work on preference handling in AI has consequently elaborated many new preference representation formalisms, as reflected by the publications at previous workshops on preference handling at AI conferences. This workshop not only continues these innovations, but brings the results back to AI problems and explores the promise of preferences for AI challenges. It seeks to increase the scope of preference handling in AI and to attract researchers from all subfields of AI to discuss potential or existing AI applications of explicit preference models.
The workshop investigates the new reasoning and problem solving capabilities of explicit preference models for all relevant subfields of AI, including multiagent systems, planning and robotics, vision and perception, natural language processing, knowledge representation and reasoning, constraint satisfaction and search, cognitive modeling and human interaction, and for AI-related fields such as social choice and consensus methods. Of particular interest are new emerging questions, for example the role of preferences for coordinating choices in multiple simultaneous tasks such as perception, reasoning, and action. Finding consensus among votes is another important topic arising in multiagent systems, data mining, and ontology formation (such as phylogenetic trees).