Probabilistic Argumentation Frameworks

Li, Hengfei; Oren, Nir; Norman, Timothy J.

doi:10.1007/978-3-642-29184-5_1

Cited by 161 publications

(183 citation statements)

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“…There are problems, however, with this approach. Hunter [7] was trying to lay some foundations for this view, following the papers [3,9]. See also a good summary in Hunter [8].…”

Section: Comparison With the Literaturementioning

confidence: 99%

Probabilistic Argumentation: An Equational Approach

Gabbay

Rodrigues

2015

Log. Univers.

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Abstract. There is a generic way to add any new feature to a system. It involves (1) identifying the basic units which build up the system and (2) introducing the new feature to each of these basic units. In the case where the system is argumentation and the feature is probabilistic we have the following. The basic units are: (a) the nature of the arguments involved; (b) the membership relation in the set S of arguments; (c) the attack relation; and (d) the choice of extensions. Generically to add a new aspect (probabilistic, or fuzzy, or temporal, etc) to an argumentation network S, R can be done by adding this feature to each component (a-d). This is a brute-force method and may yield a non-intuitive or meaningful result. A better way is to meaningfully translate the object system into another target system which does have the aspect required and then let the target system endow the aspect on the initial system. In our case we translate argumentation into classical propositional logic and get probabilistic argumentation from the translation. Of course what we get depends on how we translate. In fact, in this paper we introduce probabilistic semantics to abstract argumentation theory based on the equational approach to argumentation networks. We then compare our semantics with existing proposals in the literature including the approaches by M. Thimm and by A. Hunter. Our methodology in general is discussed in the conclusion.

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“…There are problems, however, with this approach. Hunter [7] was trying to lay some foundations for this view, following the papers [3,9]. See also a good summary in Hunter [8].…”

Section: Comparison With the Literaturementioning

confidence: 99%

Probabilistic Argumentation: An Equational Approach

Gabbay

Rodrigues

2015

Log. Univers.

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“…• In the constellations approach, the uncertainty is in the topology of the graph (Dung & Thang, 2010;Li, Oren, & Norman, 2011;Hunter, 2012;Fazzinga, Flesca, & Parisi, 2013;Li, Oren, & Norman, 2013;Hunter & Thimm, 2014a;Dondio, 2014;Polberg & Doder, 2014;Doder & Woltran, 2014;Fazzinga, Flesca, & Parisi, 2015;Liao & Huang, 2015;Hadoux, Beynier, Maudet, Weng, & Hunter, 2015;Sun & Liao, 2016;Fazzinga et al, 2016). As an example, this approach is useful when one agent is not sure what arguments and attacks another agent is aware of, and so this can be captured by a probability distribution over the space of possible argument graphs.…”

Section: Probabilistic Argumentationmentioning

confidence: 99%

“…probabilistic reasoning can be divided (Hunter, 2013) into the constellations approach (see e.g. Li, Oren, & Norman, 2011) and the epistemic approach (see e.g. Thimm, 2012).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Reasoning with Abstract Argumentation Frameworks

Hunter¹,

Thimm²

2017

jair

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argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by considering probability assignments on arguments, allowing for a quantitative treatment of formal argumentation. In this paper, we regard the assignment as denoting the degree of belief that an agent has in an argument being acceptable. While there are various interpretations of this, an example is how it could be applied to a deductive argument. Here, the degree of belief that an agent has in an argument being acceptable is a combination of the degree to which it believes the premises, the claim, and the derivation of the claim from the premises. We consider constraints on these probability assignments, inspired by crisp notions from classical abstract argumentation frameworks and discuss the issue of probabilistic reasoning with abstract argumentation frameworks. Moreover, we consider the scenario when assessments on the probabilities of a subset of the arguments are given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalise this scenario by also considering inconsistent assessments, i.e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistency-tolerant reasoning.

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“…The first is based on a probability distribution over the subgraphs of the argument graph ( [3] which extends [1] and [7]), and this can be used to represent the uncertainty over the structure of the graph (i.e. whether a particular argument or attack appears in the argument graph under consideration).…”

Section: A Brief Introduction To Probabilisticmentioning

confidence: 99%

Knowledge Science, Engineering and Management

2018

Lecture Notes in Computer Science

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Probabilistic Argumentation Frameworks

Cited by 161 publications

References 20 publications

Probabilistic Argumentation: An Equational Approach

Probabilistic Argumentation: An Equational Approach

Probabilistic Reasoning with Abstract Argumentation Frameworks

Knowledge Science, Engineering and Management

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