Rationalisation of Profiles of Abstract Argumentation Frameworks: Extended Abstract

Airiau, Stéphane; Bonzon, Elise; Endriss, Ulle; Maudet, Nicolas; Rossit, Julien

doi:10.24963/ijcai.2017/665

Cited by 4 publications

(9 citation statements)

References 2 publications

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“…. , att k } with att i := A (i) ⇀ A (i+1) for i < k and att k := A (k) ⇀ A (1) . Clearly, this set of attacks meets our requirements: (i) if {att 1 , .…”

Section: Proof Of Theoremmentioning

confidence: 99%

Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks

Chen

Endriss

2017

Electron. Proc. Theor. Comput. Sci.

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An abstract argumentation framework can be used to model the argumentative stance of an agent at a high level of abstraction, by indicating for every pair of arguments that is being considered in a debate whether the first attacks the second. When modelling a group of agents engaged in a debate, we may wish to aggregate their individual argumentation frameworks to obtain a single such framework that reflects the consensus of the group. Even when agents disagree on many details, there may well be high-level agreement on important semantic properties, such as the acceptability of a given argument. Using techniques from social choice theory, we analyse under what circumstances such semantic properties agreed upon by the individual agents can be preserved under aggregation.

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“…. , att k } with att i := A (i) ⇀ A (i+1) for i < k and att k := A (k) ⇀ A (1) . Clearly, this set of attacks meets our requirements: (i) if {att 1 , .…”

Section: Proof Of Theoremmentioning

confidence: 99%

Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks

Chen

Endriss

2017

Electron. Proc. Theor. Comput. Sci.

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“…This area of research in collective argumentation is not only restricted to finding aggregation mechanisms. For example, in the work of Airiau et al [1] the goal differentiates and instead they study the computational complexity of a problem defined as the "rationalisability problem". In this problem each agent has its own argumentation framework AF i and the aim is to identify if there are a single master argumentation framework AF , an association of arguments with values and a profile of preference orders over values that can "explain" all the AF i (i.e, AF rationalizes the set of AF i ).…”

Section: Discussionmentioning

confidence: 99%

A decision-making approach where argumentation added value tackles social choice deficiencies

Bisquert¹,

Croitoru

Kaklamanis

et al. 2019

Prog Artif Intell

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Collective decision-making in multi-agents systems is classically performed by employing social choice theory methods. Each member of the group (i.e., agent) expresses preferences as a (total) order over a given set of alternatives, and the group's aggregated preference is computed using a voting rule. Nevertheless, classic social choice methods do not take into account the rationale behind agents' preferences. Our research hypothesis is that a decision made by a group of participants understanding the qualitative rationale (expressed by arguments) behind each other's preferences has better chances to be accepted and used in practice. Accordingly, in this work, we propose a novel qualitative procedure which combines argumentation with computational social choice for modeling the collective decision-making problem. We show that this qualitative approach produces structured preferences that can overcome major deficiencies that appear in the social choice literature and affect most of the major voting rules. Hence, in this paper we deal with the Condorcet Paradox and the properties of Monotonicity and Homogeneity which are unsatisfiable by many voting rules.

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“…, E n ) specifies an attack relation for each of a number of agents that we may wish to aggregate into a collective attack relation before attempting to determine which of the arguments might be acceptable to the group. Recent work has addressed the challenge of aggregating several abstract argumentation frameworks from a number of angles, e.g., by proposing concrete aggregation methods grounded in work on belief merging [Coste-Marquis et al, 2007], by investigating the computational complexity of aggregation [Dunne et al, 2012], and by analysing what kinds of profiles we may reasonably expect to encounter in this context [Airiau et al, 2016].…”

Section: Examples Of Application Scenariosmentioning

confidence: 99%

Graph aggregation

Endriss

Grandi

2017

Artificial Intelligence

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Graph aggregation is the process of computing a single output graph that constitutes a good compromise between several input graphs, each provided by a different source. One needs to perform graph aggregation in a wide variety of situations, e.g., when applying a voting rule (graphs as preference orders), when consolidating conflicting views regarding the relationships between arguments in a debate (graphs as abstract argumentation frameworks), or when computing a consensus between several alternative clusterings of a given dataset (graphs as equivalence relations). In this paper, we introduce a formal framework for graph aggregation grounded in social choice theory. Our focus is on understanding which properties shared by the individual input graphs will transfer to the output graph returned by a given aggregation rule. We consider both common properties of graphs, such as transitivity and reflexivity, and arbitrary properties expressible in certain fragments of modal logic. Our results establish several connections between the types of properties preserved under aggregation and the choice-theoretic axioms satisfied by the rules used. The most important of these results is a powerful impossibility theorem that generalises Arrow's seminal result for the aggregation of preference orders to a large collection of different types of graphs. * This work refines and extends papers presented at COMSOC-

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Rationalisation of Profiles of Abstract Argumentation Frameworks: Extended Abstract

Cited by 4 publications

References 2 publications

Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks

Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks

A decision-making approach where argumentation added value tackles social choice deficiencies

Graph aggregation

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