Preference Modeling and Preference Elicitation: An Overview

Handling preferences [16] is important in a number of domains, including recommender systems, computational advertisement, personal cognitive assistants, systems for decision support (for example, in medicine) and robotics. Artificial intelligence has been dealing with preferences for quite some time. In order to get closer to the goal of realizing autonomous agents that can decide and act on behalf of humans, formal tools are needed in order to model preferences, represent preferences in a compact way, support reasoning, and elicit (or learn) them from the user (decision maker). Research on preference handling systems makes use of quite a variety of different tools, including formal logic, optimization techniques from operations research; there is also a substantial intersection with research in mathematical economics, especially in approaches based on utility. An additional challenge is brought by the realization that (differently from what assumed in classical economics) humans are not rational decision makers, and are prone to decision biases; this recognition is important especially if the aim is to produce systems that are meant to be used by real users. A thorough introduction to the topic of preferences in artificial intelligence, going much more in depth, can be found in [17].