The Added Value of Argumentation

Modgil, Sanjay; Toni, Francesca; Bex, Floris; Bratko, Ivan; Chesñevar, Carlos Iván; Dvořák, Wolfgang; Falappa, Marcelo Alejandro; Fan, Xiangyu; Gaggl, Sarah Alice; García, Alejandro Javier; González, María Paula; Gordon, Thomas F.; Leite, João; Možina, Martin; Reed, Chris; Simari, Guillermo Ricardo; Szeider, Stefan; Torroni, Paolo; Woltran, Stefan

doi:10.1007/978-94-007-5583-3_21

Cited by 52 publications

(29 citation statements)

References 65 publications

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“…While aspects of natural language arguments have often been the subject of choice, a more formal interpretation has emerged over the past decades to form the field of (computational) argumentation see e.g. [57,107,141]. Broadly, argumentation is concerned with the development of semantics under which we may create, use and evaluate arguments in argumentation frameworks (AFs).…”

Section: Argumentationmentioning

confidence: 99%

“…The field of computational argumentation (henceforth called just argumentation), on the other hand, has emerged as a discipline concerned with developing more formal, logic-based accounts of arguments, e.g. as in [57,107,141]. Since its inception in the late 1980s this stream of research on arguments has had a substantial impact on a number of domains.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Argumentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Argumentation to Improve Classification in Natural Language Problems

Carstens

Toni

2017

ACM Trans. Internet Technol.

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“…A number of researchers have explored argumentation based negotiation as a means of changing preference relationships during negotiations [1], negotiating with constraints [8], and deciding with incomplete, uncertain, or inconsistent information [9], [15]. A number of frameworks have been proposed [2], [12], [13], [14] and been analyzed formally [1] and empiracally [10].…”

Section: Related Workmentioning

confidence: 99%

Distributed decision making in cognitive radio networks through argumentation

Horine

Bölöni

Turgut

2013

2013 IEEE Global Communications Conference (GLOBECOM)

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Abstract-We have developed a multi-agent negotiation system to distribute decision making in cognitive radio networks through argumentation. The challenge in wireless network negotiation is to efficiently exchange information to facilitate a deal without incurring excessive communication overhead or indeterminate negotiation time. Our goal is to improve both total network throughput and the number of total supported connections. We detail a set of rules, a protocol, and a compact set of messages to conduct these negotiations and complete in finite time and with little overhead. We describe our simulation environment and present results of an illustrative scenario with various conditions. This scenario includes the ability of an agent to assert high priority, possibly triggering a downgrade of an existing, nonpriority connection to a slower rate in order to accommodate more connections. We compare our system's total network throughput, number of connections, and request satisfaction score to several baselines with various levels of reconsideration and conclude that our system outperforms these other approaches in all metrics.

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“…Argument dialogues are an established agreement technology; they provide a principled way of structuring rational interactions between participants (machine or human) who argue about the validity of certain claims in order to resolve their conflicting information, competing goals, incompatible intentions or opposing views of the world [1]. Such dialogues are typically defined by the moves that can be made and rules to determine which moves are permissible at any point in the dialogue.…”

Section: Introductionmentioning

confidence: 99%

Reasons and Options for Updating an Opponent Model in Persuasion Dialogues

Black

Hunter

2015

Theory and Applications of Formal Argumentation

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Abstract. Dialogical argumentation allows agents to interact by constructing and evaluating arguments through a dialogue. Numerous proposals have been made for protocols for dialogical argumentation, and recently there is interest in developing better strategies for agents to improve their own outcomes from the interaction by using an opponent model to guide their strategic choices. However, there is a lack of clear formal reasons for why or how such a model might be useful, or how it can be maintained. In this paper, we consider a simple type of persuasion dialogue, investigate options for using and updating an opponent model, and identify conditions under which such use of a model is beneficial.

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The Added Value of Argumentation

Cited by 52 publications

References 65 publications

Using Argumentation to Improve Classification in Natural Language Problems

Using Argumentation to Improve Classification in Natural Language Problems

Distributed decision making in cognitive radio networks through argumentation

Reasons and Options for Updating an Opponent Model in Persuasion Dialogues

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