By Francesca Rossi, Kristen Brent Venable, Toby Walsh
Computational social selection is an increasing box that merges classical themes like economics and vote casting idea with extra glossy subject matters like synthetic intelligence, multiagent structures, and computational complexity. This booklet presents a concise creation to the most learn traces during this box, masking elements reminiscent of choice modelling, uncertainty reasoning, social selection, reliable matching, and computational elements 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 offers the most ways to modeling and reasoning with personal tastes, with specific cognizance to 2 well known and robust formalisms, smooth constraints and CP-nets. The authors contemplate choice elicitation and diverse sorts of uncertainty in tender constraints. They evaluation the main suitable leads to balloting, with detailed cognizance to computational social selection. ultimately, the booklet considers personal tastes in matching difficulties. The booklet is meant for college kids and researchers who should be drawn to an advent 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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Additional info for A Short Introduction to Preferences: Between AI and Social Choice (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Such solutions are called interval dominant. 32 3. UNCERTAINTY IN PREFERENCE REASONING If we consider our running example, s1 and s4 are lower optimal, while s1 and s2 are upper optimal. This implies that only s1 is interval optimal. 2, there are no interval dominant solutions. Interestingly, these interval-based optimality notions can be related to scenarios. For example, interval dominant solutions coincide with the necessarily optimal solutions. Also the other notions can be expressed in terms of scenarios.
Given a soft CSP (the concrete one), we may get an abstract soft CSP by simplifying the associated semiring, and relating the two structures (the concrete and the abstract one) via a Galois insertion. This way of abstracting constraint problems does not change the structure of the problem (the set of variables remains the same, as well as the set of constraints), but only the semiring values to be associated with the tuples of values for the variables in each constraint . Abstraction has also been used also to simplify the solution process of hard constraint problems .
This sort of approach has also been used for preference-based configurators, using the soft version of arc consistency, whose application may decrease the preferences in some constraints (while maintaining the same semantics overall). Explanations can then describe why the preferences for some variable values decrease, and they suggest at the same time which assignments can be retracted in order to get a more preferred solution . Configurators with soft constraints help users not only avoid conflicts or make the next choice so that fewer later choices are eliminated, but also get to an optimal (or good enough) solution.