Introduction to structured argumentation

Abstract. Cumulative Transitivity and Cautious Monotonicity are widely considered as important properties of non-monotonic inference and equally as regards to information change. We propose three novel formulations of each of these properties for Assumption-Based Argumentation (ABA)-an established structured argumentation formalism, and investigate these properties under a variety of ABA semantics.

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“…Recently, some forms of structured argumentation (see [5] for an overview) have been investigated in terms of non-monotonic inference (c.f. Sect.…”

Section: Introductionmentioning

confidence: 99%

Non-Monotonic Inference Properties for Assumption-Based Argumentation

Čyras

Toni

2015

Lecture Notes in Computer Science

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“…Our exposition follows [11]. The very first issue is to choose how to represent nodes of an argumentation graph in this case.…”

Section: Towards Encoding Graphs Of Structured Argumentsmentioning

confidence: 99%

Encoding Argument Graphs in Logic

Besnard

Doutre

Herzig

2014

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12983 Any correspondance concerning this service should be sent to the repository administrator: staff-oatao@listes-diff.inp-toulouse.fr Encoding Argument Graphs in LogicPhilippe Besnard, Sylvie Doutre, and Andreas Herzig IRIT-CNRS, University of Toulouse, France {besnard,doutre,herzig}@irit.frAbstract. Argument graphs are a common way to model argumentative reasoning. For reasoning or computational purposes, such graphs may have to be encoded in a given logic. This paper aims at providing a systematic approach for this encoding. This approach relies upon a general, principle-based characterization of argumentation semantics.

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“…Whereas in abstract AFs the internal structure of an argument is of no relevance, structured argumentation [18] covers a range of AFs where some sort of structure is imposed upon individual arguments. In Assumption-Based Argumentation (ABA) [58,211] arguments are identified as deductions, supported by assumptions and rules, and attacks in terms of contraries of assumptions, where assumptions are premises that form the support in arguments for conclusions.…”

Section: Other Argumentation Frameworkmentioning

confidence: 99%

Using Argumentation to Improve Classification in Natural Language Problems

Carstens

Toni

2017

ACM Trans. Internet Technol.

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Argumentation has proven successful in a number of domains, including multi-agent systems and decision support in medicine and engineering. We propose its application to a domain yet largely unexplored by argumentation research: computational linguistics. Over the past decade or so advances in this field have commonly relied on data-driven solutions, i.e. machine learning. Recently, however, there appears to be a growing consent that, in order to achieve significant advances in certain areas of artificial intelligence, in general, and computational linguistics, in particular, we may need to consider data-driven approaches in unison with reasoning-, logic-and rule-based solutions. To this end we have developed a novel classification methodology that incorporates reasoning through argumentation with supervised classifiers. We train classifiers and then argue about the validity of their output.To do so we identify arguments that formalise prototypical knowledge of a problem and use them to correct misclassifications. We thus have at our disposal multiple ways of incorporating knowledge in classification and are able to integrate knowledge as it becomes available, without the need to retrain classifiers.We illustrate our methodology on two tasks. On the one hand we address binary cross-domain sentiment polarity classification, where we train classifiers on one corpus, e.g. Tweets, to identify positive/negative polarity, and classify instances from another corpus, e.g. sentences from movie reviews. On the other hand we address a form of argumentation mining that we call Relation-based Argumentation Mining, where we classify pairs of sentences based on whether the first sentence attacks or supports the second, or whether it does neither. Whenever we find that one sentence attacks/supports the other we consider both to be argumentative, irrespective of their stand-alone argumentativeness. For both tasks we improve classification performance when using our methodology, compared to using standard classifiers, only.3

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Introduction to structured argumentation

Cited by 120 publications

References 1 publication

Non-Monotonic Inference Properties for Assumption-Based Argumentation

Non-Monotonic Inference Properties for Assumption-Based Argumentation

Encoding Argument Graphs in Logic

Using Argumentation to Improve Classification in Natural Language Problems

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