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When we design an agent that automatically shops on the web or controls a rover on Mars, we don't want it to buy any item or conduct any experiment. We want it to buy the best available item and conduct the most useful experiment. In short, we want it to act optimally. But acting optimally on behalf of a user requires understanding of that user's goals and preferences. How can an agent obtain this information efficiently when acting on behalf of a lay user? How can this be done with a minimal effort on the part of the user? How does one represent preference information compactly and reasons with it effectively?
These questions drive the research conducted in the area of preference modeling, elicitation, representation, and reasoning techniques. This tutorial surveys some of the major classical and recent developments in this area. We focus, in particular, on alternative representations and languages for preferences, such as compact value functions, qualitative languages and cp-nets, and preference compilation. Our goal is to provide a unified and coherent picture of the field, the various directions pursued so far, and the main research issues. No special background in the area is assumed.