Proof Theories and Algorithms for Abstract Argumentation Frameworks

Modgil, Sanjay; Caminada, Martin

doi:10.1007/978-0-387-98197-0_6

Cited by 147 publications

(180 citation statements)

References 23 publications

Supporting

Mentioning

178

Contrasting

Unclassified

Order By: Relevance

“…An algorithm for generating the grounded {in, out, un}-labelling of an argumentation graph is given in Algorithm 1 (Modgil and Caminada, 2009). It begins by labelling in all arguments not being attacked or whose attackers are out (line 4), and then it iteratively labels out any argument attacked by an argument labelled in (line 5).…”

Section: Abstract Argumentation Frameworkmentioning

confidence: 99%

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Riveret¹,

Korkinof²,

Draief³

et al. 2015

Argument & Computation

View full text Add to dashboard Cite

Probabilistic argumentation and neuro-argumentative systems offer new computational perspectives for the theory and applications of argumentation, but their principled construction involves two entangled problems. On the one hand, probabilistic argumentation aims at combining the quantitative uncertainty addressed by probability theory with the qualitative uncertainty of argumentation, but probabilistic dependences amongst arguments as well as learning are usually neglected. On the other hand, neuro-argumentative systems offer the opportunity to couple the computational advantages of learning and massive parallel computation from neural networks with argumentative reasoning and explanatory abilities, but the relation of probabilistic argumentation frameworks with these systems has been ignored so far. Towards the construction of neuro-argumentative systems based on probabilistic argumentation, we associate a model of abstract argumentation and the graphical model of Boltzmann machines (BMs) in order to (i) account for probabilistic abstract argumentation with possible and unknown probabilistic dependences amongst arguments, (ii) learn distributions of labellings from a set of cases and (iii) sample labellings according to the learned distribution. Experiments on domain independent artificial datasets show that argumentative BMs can be trained with conventional training procedures and compare well with conventional machines for generating labellings of arguments, with the assurance of generating grounded labellingson demand.

show abstract

Section: Abstract Argumentation Frameworkmentioning

confidence: 99%

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Riveret¹,

Korkinof²,

Draief³

et al. 2015

Argument & Computation

View full text Add to dashboard Cite

show abstract

“…An example of such a game is the following (which is the game for Dung's 1995 so-called grounded semantics; cf. Prakken and Sartor 1997;Modgil and Caminada 2009). The proponent starts with the argument to be tested and then the players take turns: at each turn the players must defeat the other player's last argument: moreover, the proponent must do so with a stronger argument, i.e., his argument may not in turn be defeated by its target.…”

Section: Argumentation Logicsmentioning

confidence: 99%

Argumentation Without Arguments

Prakken

2011

Argumentation

View full text Add to dashboard Cite

A well-known ambiguity in the term 'argument' is that of argument as an inferential structure and argument as a kind of dialogue. In the first sense, an argument is a structure with a conclusion supported by one or more grounds, which may or may not be supported by further grounds. Rules for the construction and criteria for the quality of arguments in this sense are a matter of logic. In the second sense, arguments have been studied as a form of dialogical interaction, in which human or artificial agents aim to resolve a conflict of opinion by verbal means. Rules for conducting such dialogues and criteria for their quality are part of dialogue theory. Usually, formal accounts of argumentation dialogues in logic and artificial intelligence presuppose an argument-based logic. That is, the ways in which dialogue participants support and attack claims are modelled as the construction of explicit arguments and counterarguments (in the inferential sense). However, in this paper formal models of argumentation dialogues are discussed that do not presuppose arguments as inferential structures. The motivation for such models is that there are forms of inference that are not most naturally cast in the form of arguments (such as abduction, statistical reasoning and coherence-based reasoning) but that can still be the subject of argumentative dialogue. Some recent work in artificial intelligence is discussed which embeds non-argumentative inference in an argumentative dialogue system, and some general observations are drawn from this discussion.

show abstract

“…It has been argued that proof dialogues, while providing equivalent results to standard argumentation semantics, can decrease the gap between intuitive and formal accounts of argumentation [1,[11][12][13] , and have been used in human-computer interactions to aid understanding [16,6,4]. While the credulous acceptance problem under preferred semantics has been modelled using dialogue games in the past [16,7,12,3], the skeptical preferred semantics has received less attention.…”

Section: Introductionmentioning

confidence: 99%

“…While the credulous acceptance problem under preferred semantics has been modelled using dialogue games in the past [16,7,12,3], the skeptical preferred semantics has received less attention.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a dialogue game for skeptical preferred acceptance that is similar to [16,10], but (i) differs from [16] in that it is not restricted to cases when the preferred and stable semantics coincide; (ii) differs to approaches such as [12,8] in that it does not use a meta-dialogue based approach; and (iii) also differs from existing approaches in that it uses argument labellings within the dialogue. Moreover, we believe that this dialogue is more intuitive than [8,10,12,16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Two-Phase Dialogue Game for Skeptical Preferred Semantics

Shams

Oren

2016

Logics in Artificial Intelligence

View full text Add to dashboard Cite

Abstract. In this paper we propose a labelling based dialogue game for determining whether a single argument within a Dung argumentation framework is skeptically preferred. Our game consists of two phases, and determines the membership of a single argument within the extension, assuming optimal play by dialogue participants. In the first phase, one player attempts to advance arguments to construct an extension not containing the argument under consideration, while the second phase verifies that the extension is indeed a preferred one. Correctness within this basic game requires perfect play by both players, and we therefore also introduce an overarching game to overcome this limitation.

show abstract

Proof Theories and Algorithms for Abstract Argumentation Frameworks

Cited by 147 publications

References 23 publications

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Probabilistic abstract argumentation: an investigation with Boltzmann machines

Argumentation Without Arguments

A Two-Phase Dialogue Game for Skeptical Preferred Semantics

Contact Info

Product

Resources

About