By Francesca Rossi, Kristen Brent Venable, Toby Walsh
Computational social selection is an increasing box that merges classical issues like economics and vote casting thought with extra smooth subject matters like synthetic intelligence, multiagent platforms, and computational complexity. This e-book presents a concise advent to the most learn traces during this box, overlaying points reminiscent of choice modelling, uncertainty reasoning, social selection, strong matching, and computational features of choice aggregation and manipulation. The e-book is situated round the inspiration 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 cognizance to 2 renowned and strong formalisms, tender constraints and CP-nets. The authors ponder choice elicitation and numerous varieties of uncertainty in smooth constraints. They evaluate the main suitable ends up in balloting, with distinctive realization to computational social selection. ultimately, the ebook considers personal tastes in matching difficulties. The ebook is meant for college kids and researchers who can be attracted to an creation to choice reasoning and multi-agent choice aggregation, and who need to know the elemental notions and ends up in computational social selection. desk of Contents: creation / choice Modeling and Reasoning / Uncertainty in choice Reasoning / Aggregating personal tastes / good 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)
Such justifications are especially useful when the user is interested in what happens at any time during search because he/she can alter features of the problem to ease the problem solving process. Basically, the aim of an explanation is to show clearly why a system acted in a certain way after certain events. Explanations have been used for hard constraint problems, especially in the context of over-constrained problems [6, 112, 113], to understand why the problem does not have a solution and what can be modified in order to get one.
All possible instances of this schema, obtained by selecting different elicitation strategies, have been tested on randomly generated soft constraint problems (fuzzy and weighted), by varying the number of variables, the tightness and density of constraints, as well as the percentage of missing preferences.
On the other hand, soft constraint networks can represent any partial order over solutions. Thus, when we are interested in the solution ordering, CP-nets and soft constraints are incomparable. This continues to hold also when we augment the CP-nets with set of hard constraints for filtering out infeasible assignments. 20 2. 2 APPROXIMATING CP-NETS VIA SOFT CONSTRAINTS It is possible to approximate a CP-net ordering via soft constraints, achieving tractability while sacrificing precision to some degree.