Assumption-Based Argumentation for Decision-Making with Preferences: A Medical Case Study

2014

Argument & Computation

Self Cite

144

137

We give an introductory tutorial to assumption-based argumentation (referred to as ABA) -a form of argumentation where arguments and attacks are notions derived from primitive notions of rules in a deductive system, assumptions and contraries thereof. ABA is equipped with different semantics for determining 'winning' sets of assumptions and -interchangeably and equivalently -'winning'sets of arguments. It is also equipped with a catalogue of computational techniques to determine whether given conclusions can be supported by a 'winning' set of arguments. These are in the form of disputes between (fictional) proponent and opponent players, provably correct w.r.t. the semantics. Albeit simple, ABA is powerful in that it can be used to represent and reason with a number of problems in AI and beyond: non-monotonic reasoning, preferences, decisions. While doing so, it encompasses the expressive and computational needs of these problems while affording the transparency and explanatory power of argumentation.

Section: Downloaded By [University Of Windsor] At 05:22 21 August 2014mentioning

confidence: 98%

A tutorial on assumption-based argumentation

2014

Argument & Computation

Self Cite

144

137

“…In previous work, the current authors have worked on several practical applications of structured argumentation (Craven et al, 2012;Fan et al, 2013Fan et al, , 2014Zhong et al, 2014) to domains including medicine and law. We conducted an analysis of the argumentation frameworks for these applications, 5 which showed that none of them contained rules which allow the construction of cyclical arguments like a 2 .…”

Section: Motivationmentioning

confidence: 99%

Argument graphs and assumption-based argumentation

Craven

Artificial Intelligence

2016

Self Cite

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.

“…[30,69], and decision support in domains such as medicine, e.g. [68,72], and engineering, e.g. [13,64].…”

Section: Introductionmentioning

confidence: 99%

Using Argumentation to Improve Classification in Natural Language Problems

Carstens

ACM Trans. Internet Technol.

2017

Self Cite

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