By Francesca Rossi, Kristen Brent Venable, Toby Walsh

ISBN-10: 1608455866

ISBN-13: 9781608455867

Computational social selection is an increasing box that merges classical themes like economics and balloting conception with extra glossy themes like man made intelligence, multiagent platforms, and computational complexity. This ebook presents a concise creation to the most study strains during this box, protecting points similar to choice modelling, uncertainty reasoning, social selection, reliable matching, and computational points of choice aggregation and manipulation. The booklet is established round the proposal of choice reasoning, either within the single-agent and the multi-agent environment. It provides the most methods to modeling and reasoning with personal tastes, with specific recognition to 2 well known and robust formalisms, smooth constraints and CP-nets. The authors reflect on choice elicitation and numerous different types of uncertainty in smooth constraints. They overview the main suitable leads to balloting, with unique recognition to computational social selection. eventually, the e-book considers personal tastes in matching difficulties. The ebook is meant for college students and researchers who could be attracted to an advent to choice reasoning and multi-agent choice aggregation, and who need to know the fundamental notions and ends up in computational social selection. desk of Contents: creation / choice Modeling and Reasoning / Uncertainty in choice Reasoning / Aggregating personal tastes / reliable Marriage difficulties

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Extra 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 first one is to do the best that we can with the available data without further bothering the user. This translates to looking for solutions that are of high quality with respect to the preferences that are known and “robust” with respect to the ones that are missing. The second strategy is to resort to elicitation, that is, to ask the user for the missing preferences. Eliciting preferences takes time and effort, and users may be reluctant to provide their preferences due to privacy concerns or annoyance, especially when confronted with large combinatorial candidate sets.

3 CP-NETS AND HARD CONSTRAINTS CP-nets provide a very intuitive framework to specify conditional qualitative preferences, but they do not allow for hard constraints. However, real-life problems often contain both constraints and preferences. While hard constraints and quantitative preferences can be adequately modeled and handled within the soft constraint formalisms, when we have both hard constraints and qualitative preferences, we need to either extend the CP-net formalism [22] or at least develop specific solution algorithms.

Moreover, as far as accommodations, which can be in a standard room, a suite, or a bungalow, assume that a suite in the Maldives is too expensive while a standard room in the Caribbean is not special enough for a honeymoon. To model this new information, we use a variable A (standing for Accommodation) with domain D(A) = {r, su, b} (r stands for room, su for suite and b for bungalow), and three constraints: two unary incomplete soft constraints, idef 1, {T } , idef 2, {D} and a binary incomplete soft constraint idef 3, {A, D} .

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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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