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Francesca Rossi, Kristen Brent Venable, Toby Walsh's A Short Introduction to Preferences: Between AI and Social PDF

By Francesca Rossi, Kristen Brent Venable, Toby Walsh

ISBN-10: 1608455866

ISBN-13: 9781608455867

Computational social selection is an increasing box that merges classical issues like economics and vote casting idea with extra sleek themes like man made intelligence, multiagent structures, and computational complexity. This booklet offers a concise advent to the most examine traces during this box, protecting elements equivalent to choice modelling, uncertainty reasoning, social selection, strong matching, and computational features of choice aggregation and manipulation. The booklet is situated round the suggestion of choice reasoning, either within the single-agent and the multi-agent atmosphere. It provides the most techniques to modeling and reasoning with personal tastes, with specific consciousness to 2 renowned and strong formalisms, gentle constraints and CP-nets. The authors ponder choice elicitation and diverse different types of uncertainty in smooth constraints. They evaluation the main suitable ends up in vote casting, with detailed realization to computational social selection. eventually, the e-book considers personal tastes in matching difficulties. The booklet is meant for college students and researchers who could be attracted to an creation to choice reasoning and multi-agent choice aggregation, and who need to know the elemental notions and leads to computational social selection. desk of Contents: creation / choice Modeling and Reasoning / Uncertainty in choice Reasoning / Aggregating personal tastes / solid 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)

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The learnt preferences are often used for preference prediction, that is, to predict the preferences of a new individual or of the same individual in a new situation. This connects preference learning to several application domains, such as collaborative filtering [103, 177] and recommender systems [2, 133, 173]. 7 OTHER PREFERENCE MODELING FRAMEWORKS There are several other ways to model preferences, besides soft constraints and CP-nets. We will briefly describe some of them in this section. Max SAT.

Possibly) optimal solutions of P . Notice that, while P OS(P ) is never empty, in general N OS(P ) may be empty. In particular, NOS(P ) is empty whenever the available preferences are not sufficient to establish the emergence of an assignment as unconditionally optimal. 3, we can easily see that NOS(P ) = ∅ since, given any assignment, it is possible to construct a completion of P in which it is not optimal. On the other hand, P OS(P ) contains all assignments not including tuple T = sh, D = m .

Also, the notion of value interchangeability has been exploited to support abstraction and reformulation of hard constraint problems [77]. In the case of hard constraints, abstracting a constraint problem means dealing with fewer variables and smaller domains. Once we obtain some information about the abstracted version of a problem, we can bring back to the original problem some (or possibly all) of the information derived in the abstract context, and then continue the solution process on the transformed problem, which is a equivalent to the original.

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A Short Introduction to Preferences: Between AI and Social Choice (Synthesis Lectures on Artificial Intelligence and Machine Learning) by Francesca Rossi, Kristen Brent Venable, Toby Walsh


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