A tutorial on assumption-based argumentation

Toni, Francesca

doi:10.1080/19462166.2013.869878

Cited by 140 publications

(144 citation statements)

References 35 publications

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“…However, there are also formalisms for structured argumentation, such as deductive argumentation (Besnard & Hunter, 2008), ASPIC + (Modgil & Prakken, 2014), ABA (Toni, 2014), and defeasible logic programming (Garcia & Simari, 2004). In structured argumentation, arguments are a set of (e.g.…”

Section: Preliminariesmentioning

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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Section: Preliminariesmentioning

confidence: 99%

Probabilistic Reasoning with Abstract Argumentation Frameworks

Hunter¹,

Thimm²

2017

jair

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“…negates the support of the argument). A range of options for structured argumentation at the logic level have been investigated (see [17,51,104,140] for tutorial reviews of some of the key proposals. Whilst most proposals for structured argumentation involve simple rule-based reasoning, there is some investigation of the role of classical logic in argumentation (see for example [16]), and of how probabilistic reasoning can be incorporated in structured arguments (see for example [74,139,144]).…”

Section: Logical Levelmentioning

confidence: 99%

Towards a framework for computational persuasion with applications in behaviour change1

Hunter

2018

AAC

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Abstract. Persuasion is an activity that involves one party trying to induce another party to believe something or to do something. It is an important and multifaceted human facility. Obviously, sales and marketing is heavily dependent on persuasion. But many other activities involve persuasion such as a doctor persuading a patient to drink less alcohol, a road safety expert persuading drivers to not text while driving, or an online safety expert persuading users of social media sites to not reveal too much personal information online. As computing becomes involved in every sphere of life, so too is persuasion a target for applying computer-based solutions. An automated persuasion system (APS) is a system that can engage in a dialogue with a user (the persuadee) in order to persuade the persuadee to do (or not do) some action or to believe (or not believe) something. To do this, an APS aims to use convincing arguments in order to persuade the persuadee. Computational persuasion is the study of formal models of dialogues involving arguments and counterarguments, of user models, and strategies, for APSs. A promising application area for computational persuasion is in behaviour change. Within healthcare organizations, government agencies, and non-governmental agencies, there is much interest in changing behaviour of particular groups of people away from actions that are harmful to themselves and/or to others around them.

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“…Both abstract argumentation and ABA define various alternative semantics and corresponding kinds of acceptable extensions. In the case of ABA, extensions can be equivalently understood in terms of sets of assumptions (in the support of arguments in acceptable extensions)-see (Bondarenko et al, 1997;Dung et al, 2007;Toni, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Assumption-Based Argumentation (ABA) (Bondarenko et al, 1997;Dung et al, 2006Dung et al, , 2007Dung et al, , 2009Toni, 2013Toni, , 2014) is a well-known form of structured argumentation. In ABA, arguments are obtained from the rules of a given deductive system and assumptions (special sentences in the language underlying the deductive system).…”

Section: Introductionmentioning

confidence: 99%

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Argument graphs and assumption-based argumentation

Craven

Toni

2016

Artificial Intelligence

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Arguments in structured argumentation are usually defined as trees, and extensions as sets of such treebased arguments with various properties depending on the particular argumentation semantics. However, these arguments and extensions may have redundancies as well as circularities, which are conceptually and computationally undesirable. Focusing on the specific case of Assumption-Based Argumentation (ABA), we propose novel notions of arguments and admissible/grounded extensions, both defined in terms of graphs. We show that this avoids the redundancies and circularities of standard accounts, and set out the relationship to standard tree-based arguments and admissible/grounded extensions (as sets of arguments). We also define new notions of graph-based admissible/grounded dispute derivations for ABA, for determining whether specific sentences hold under the admissible/grounded semantics. We show that these new derivations are superior with respect to standard dispute derivations in that they are complete in general, rather than solely for restricted classes of ABA frameworks. Finally, we present several experiments comparing the implementation of graph-based admissible/grounded dispute derivations with implementations of standard dispute derivations, suggesting that the graph-based approach is computationally advantageous.

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A tutorial on assumption-based argumentation

Cited by 140 publications

References 35 publications

Probabilistic Reasoning with Abstract Argumentation Frameworks

Probabilistic Reasoning with Abstract Argumentation Frameworks

Towards a framework for computational persuasion with applications in behaviour change1

Argument graphs and assumption-based argumentation

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