By Francesca Rossi, Kristen Brent Venable, Toby Walsh
Computational social selection is an increasing box that merges classical issues like economics and vote casting concept with extra glossy issues like man made intelligence, multiagent platforms, and computational complexity. This e-book presents a concise creation to the most examine strains during this box, protecting features comparable to choice modelling, uncertainty reasoning, social selection, good matching, and computational features of choice aggregation and manipulation. The e-book is established 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 cognizance to 2 renowned and robust formalisms, gentle constraints and CP-nets. The authors examine choice elicitation and numerous different types of uncertainty in delicate constraints. They evaluation the main proper ends up in vote casting, with detailed cognizance 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 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: creation / choice Modeling and Reasoning / Uncertainty in choice Reasoning / Aggregating personal tastes / solid Marriage difficulties
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Extra resources for A Short Introduction to Preferences: Between AI and Social Choice (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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. An added difficulty in dealing with soft constraints is related to the modeling phase, where a user has to understand how to faithfully model his real-life problem via soft constraints.
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 . Sometimes weights are replaced by a priority relation among the goals. Often with prioritized goals candidates are evaluated via the so-called discrimin ordering: a candidate x is better than another candidate y when, for each goal g satisfied by y and violated by x, there is a goal g satisfied by x and violated by y such that g has priority over g.
For example, intervals are used in  to deal with unstable costs, which are present in many real-life problems. A typical example is the budget estimate for next year in a company. Such an estimate may be based on data which is not known or not certain, and most of the time such uncertainty is represented as last year’s value for that kind of data (which can be seen as the default value), plus some range of possible other values around the default value. Another kind of problem where unstable values may occur is when we want to represent linguistic concepts numerically, such as "more or less", "around", "at least", or "at most".