By Francesca Rossi, Kristen Brent Venable, Toby Walsh
Computational social selection is an increasing box that merges classical themes like economics and balloting conception with extra smooth themes like man made intelligence, multiagent platforms, and computational complexity. This publication offers a concise creation to the most learn traces during this box, overlaying features reminiscent of choice modelling, uncertainty reasoning, social selection, good matching, and computational points of choice aggregation and manipulation. The booklet is situated round the suggestion of choice reasoning, either within the single-agent and the multi-agent surroundings. It provides the most ways to modeling and reasoning with personal tastes, with specific awareness to 2 well known and robust formalisms, gentle constraints and CP-nets. The authors think about choice elicitation and diverse kinds of uncertainty in delicate constraints. They evaluation the main correct ends up in vote casting, with distinct recognition to computational social selection. ultimately, the ebook considers personal tastes in matching difficulties. The publication is meant for college kids and researchers who could be drawn to an advent to choice reasoning and multi-agent choice aggregation, and who need to know the fundamental notions and leads to computational social selection. desk of Contents: advent / choice Modeling and Reasoning / Uncertainty in choice Reasoning / Aggregating personal tastes / good Marriage difficulties
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Additional info for A Short Introduction to Preferences: Between AI and Social Choice (Synthesis Lectures on Artificial Intelligence and Machine Learning)
In all these cases, the natural formulation is to have a value and a range around (or above, or below) such a value. A possible approach to deal with this kind of problems is that of robust optimization , that is, to cast them into uncertain linear programming problems, uncertain conic quadratic and semi-definite optimization problems, or dynamic (multi-stage) problems. In , instead, a framework similar to that of IVSCSPs is considered in which the user can additionally provide an interval of preferences, indicate an element in such an interval which should be considered as the “default” (or most likely) value.
We now present a framework that deals with an even stronger assumption: that some preferences are completely missing . In some settings, users may know all the preferences but are willing to reveal only some of them at the beginning. Although some of the preferences can be missing, it could still be feasible to find an optimal solution. If not, the idea is to ask the user to provide some of the missing preferences and to try to solve the new problem. As in the case of intervals, we have a set of variables V with finite domain D.
The utility of a candidates is then computed by collecting the weights of satisfied and violated goals, and then aggregating them. Often only violated goals count, and their utilities are aggregated with functions such as sum or maximin. In other cases, we may sum the weights of the satisfied goals, or we may take their maximum weight. Any restriction we may impose on the goals or the weights, and any choice of an aggregation function, give a different language. Such languages may have drastically different properties in terms of their expressivity, succinctness, and computational complexity .