By Francesca Rossi, Kristen Brent Venable, Toby Walsh
Computational social selection is an increasing box that merges classical issues like economics and balloting conception with extra glossy issues like man made intelligence, multiagent platforms, and computational complexity. This ebook presents a concise advent to the most study strains during this box, masking features akin to choice modelling, uncertainty reasoning, social selection, sturdy matching, and computational features of choice aggregation and manipulation. The publication is headquartered round the proposal of choice reasoning, either within the single-agent and the multi-agent atmosphere. It provides the most methods to modeling and reasoning with personal tastes, with specific awareness to 2 well known and robust formalisms, gentle constraints and CP-nets. The authors think of choice elicitation and diverse kinds of uncertainty in smooth constraints. They evaluation the main correct leads to vote casting, with designated consciousness to computational social selection. eventually, the e-book considers personal tastes in matching difficulties. The e-book is meant for college students and researchers who will be attracted to an creation 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 resources for A Short Introduction to Preferences: Between AI and Social Choice (Synthesis Lectures on Artificial Intelligence and Machine Learning)
N. Here means preferred to. 5 a). This CP-net has three features, which are graphically represented by nodes, of which two (main course and fruit) do not depend on any other feature, while wine depends on main course. The dependency is graphically represented by the arrow. For the independent features, we just have a total order over their values (such as fish meat for main course), while for the dependent feature we have a total order for each assignment of the feature it depends upon (thus one for main course = fish and another one for main course = meat).
In many cases, we may end up with a soft constraint problem where some preferences are missing, for example because they are too costly to be computed or because of privacy reasons. To reason in such scenarios, we may use techniques like machine learning and preference elicitation to solve the given problem. 1 ABSTRACTION TECHNIQUES As noted above, soft constraints are more expressive than hard constraints, but it is also more difficult to model and solve a soft constraint problem. Therefore, sometimes it may be too costly to find all, or even a single, optimal solution.
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 . In the context of missing preferences, completions play a fundamental role. Among the many ways in which the missing preferences can be completed, let us focus on the completions obtained replacing ? everywhere with the worst possible (respectively, the best possible) preference.