Preferences, Constraints, Uncertainty, and Multi-Agent Scenarios
Francesca Rossi · 2008
Preferences occurr in many everyday tasks. Whether we look for a house, a car, a computer, a digital camera, or a vacation package, we usually state our own preferences and we expect to find the item of our dreams. It is therefore natural that modelling and solving sets of preferences is a central issue in AI, if we want to understand human intelligence and use computing devices to replicate some of functions of the human brain. This paper will discuss different kinds of preferences, will describe and compare some of the AI formalisms to model preferences, and will hint at existing preference solvers. Uncertainty will also be considered, in the form of a possibly incomplete set of preferences, because of privacy issues or missing data. We will also discuss multi-agent settings where possibly incomplete preferences need to be aggregated, and will present results related to both normative and computational properties of such systems. While the results on single-agent preference solving are mostly related to AI sub-areas such as constraint programming and knowledge representation, those on multi-agent preference aggregation are multi-disciplinary, since preference aggregation and its properties have been extensively studied also in in decision theory, economy, and political sciences.