By Francesca Rossi, Kristen Brent Venable, Toby Walsh
Computational social selection is an increasing box that merges classical issues like economics and balloting idea with extra glossy themes like synthetic intelligence, multiagent platforms, and computational complexity. This publication offers a concise advent to the most learn strains during this box, overlaying elements resembling choice modelling, uncertainty reasoning, social selection, good matching, and computational facets of choice aggregation and manipulation. The ebook is based round the suggestion of choice reasoning, either within the single-agent and the multi-agent surroundings. It offers the most techniques to modeling and reasoning with personal tastes, with specific consciousness to 2 well known and strong formalisms, delicate constraints and CP-nets. The authors examine choice elicitation and diverse kinds of uncertainty in gentle constraints. They evaluate the main suitable leads to balloting, with designated recognition to computational social selection. ultimately, the e-book considers personal tastes in matching difficulties. The publication is meant for college students and researchers who should 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 / 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)
This is intended to mean that two variable assignments that differ for the value of one variable, while all else is the same, are always ordered (that is, one is preferred to the other one). To understand which is preferred to which, we just have to look at the CP-table of the changing variable. 5 a), assignment f ish, white, peaches is preferred to f ish, red, peaches since the CP-table of variable wine tells us that, when there is fish, we prefer white wine to red wine. In order to compute the entire ordering over all variable assignments, we use the concept of a worsening flip.
6 ABSTRACTING, EXPLAINING, AND ELICITING PREFERENCES In constraint satisfaction problems, we look for a solution, while in soft constraint problems, we look for an optimal solution. Not surprisingly, soft constraint problems are typically more difficult to solve. To ease this difficulty, several AI techniques have been used. Here we discuss just two of them: abstraction and explanation generation. Abstraction works on a simplified version of the given problem, thus hoping to have a significantly smaller search space, while explanation generation helps understand the result of the solver: it is not always easy for a user to understand why no better solution is returned.
It is often desirable that such approximations are information preserving; that is, what is ordered in the given ordering is also ordered in the same way in the approximation. In this way, when two outcomes are ordered in the approximation, we can infer that they are either ordered or incomparable in the CP-net. If instead two outcomes are in a tie or incomparable in the approximation, they are surely incomparable in the CP-net. Another desirable property of approximations is that they preserve the ceteris paribus property.