Representing and Reasoning with Preferences

Rossi, Francesca

doi:10.1007/978-3-540-30227-8_1

Cited by 28 publications

(84 citation statements)

References 2 publications

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“…For instance, if we are using CP-nets, we first elicit a CP-net for each voter, these CPnets are then aggregated so as to output an outcome or a set of outcomes. See [19] and [22] for two approaches for CP-net aggregation (see also [12] for group decision making based on the aggregation of GAI-nets).…”

Section: Group Decision Makingmentioning

confidence: 99%

Graphical Representation of Ordinal Preferences: Languages and Applications

Lang

2010

Lecture Notes in Computer Science

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Section: Group Decision Makingmentioning

confidence: 99%

Graphical Representation of Ordinal Preferences: Languages and Applications

Lang

2010

Lecture Notes in Computer Science

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“…An overview of representation and computational issues in social choice is reported in [48]. One of the most popular ways of reaching common decisions is voting, which for instance has recently been used in [67] to reach consensus in MultiAgent Systems, by representing qualitative and conditional preferences by means of the mCP nets formalism, which is a graphical formalism based on CP nets [26].…”

Section: Distributed Rational Decision Makingmentioning

confidence: 99%

Solving abduction by computing joint explanations

Greco

2007

Ann Math Artif Intell

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An extension of abduction is investigated where explanations are jointly computed by sets of interacting agents. On the one hand, agents are allowed to partially contribute to the reasoning task, so that joint explanations can be singled out even if each agent does not have enough knowledge for carrying out abduction on its own. On the other hand, agents maintain their autonomy in choosing explanations, each one being equipped with a weighting function reflecting its perception about the reliability of sets of hypotheses. Given that different agents may have different and possibly contrasting preferences on the hypotheses to be chosen, some reasonable notions of agents' agreement are introduced, and their computational properties are thoroughly studied. As an example application of the framework discussed in the paper, it is shown how to handle data management issues in Peer-to-Peer systems and, specifically, how to provide a repair-based semantics to inconsistent ones.

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“…While a voting rule outputs a single candidate, an aggregation function outputs a collective preference relation or utility function. Ideally, if the voters' preferences are represented in some language, we would like the output to be represented in the same language: for instance, we would want to aggregate a collection of GAI-nets into a GAI-net (Gonzales, Perny, & Queiroz 2006), or similarly for CP-nets (Rossi, Venable, & Walsh 2004).…”

Section: Preference Aggregationmentioning

confidence: 99%

Preference Handling in Combinatorial Domains: From AI to Social Choice

et al. 2008

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In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains, and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, known as computational social choice, that brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

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Representing and Reasoning with Preferences

Cited by 28 publications

References 2 publications

Graphical Representation of Ordinal Preferences: Languages and Applications

Graphical Representation of Ordinal Preferences: Languages and Applications

Solving abduction by computing joint explanations

Preference Handling in Combinatorial Domains: From AI to Social Choice

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