Argumentation-Based Reasoning in BDI Agents Using Toulmin's Model

Gabriel, Vágner de Oliveira; Adamatti, Diana Francisca; Panisson, Alison R.; Bordini, Rafael H.; Billa, Cléo Zanella

doi:10.1109/bracis.2018.00072

Cited by 4 publications

(1 citation statement)

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“…Toulmin's model of argument has been examined in different of fields, including law and computer science. For instance, in multi-agent systems, multiple agents collaborate to make decisions and inferences to accomplish specific goals [8], where it can be used to generate argument, as in Gabriel et al's Belief-Desire-Intention software model (BDI) agents based on Toulmin's models [8], [9]. The warrant is an implicit (or major) premise in the Toulmin argument model that explains how a conclusion (or claim) is deduced from the given premises (or evidence) [10].…”

Section: Related Workmentioning

confidence: 99%

Warrant Generation through Deep Learning

Alkhawaldeh¹,

Yuan²,

Kazakov³

2021

Natural Language Computing

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The warrant element of the Toulmin model is critical for fact-checking and assessing the strength of an argument. As implicit information, warrants justify the arguments and explain why the evidence supports the claim. Despite the critical role warrants play in facilitating argument comprehension, the fact that most works aim to select the best warrant from existing structured data and labelled data is scarce presents a fact-checking challenge, particularly when the evidence is insufficient, or the conclusion is not inferred or generated well based on the evidence. Additionally, deep learning methods for false information detection face a significant bottleneck due to their training requirement of a large amount of labelled data. Manually annotating data, on the other hand, is a time-consuming and laborious process. Thus, we examine the extent to which warrants can be retrieved or reconfigured using unstructured data obtained from their premises.

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