On computing explanations in argumentation

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Argumentation can be viewed as a process of generating explanations. However, existing argumentation semantics are developed for identifying acceptable arguments within a set, rather than giving concrete justifications for them. In this work, we propose a new argumentation semantics, related admissibility, designed for giving explanations to arguments in both Abstract Argumentation and Assumption-based Argumentation. We identify different types of explanations defined in terms of the new semantics. We also give a correct computational counterpart for explanations using dispute forests

Explanation for case-based reasoning via abstract argumentation

Case-based reasoning (CBR) is extensively used in AI in support of several applications, to assess a new situation (or case) by recollecting past situations (or cases) and employing the ones most similar to the new situation to give the assessment. In this paper we study properties of a recently proposed method for CBR, based on instantiated Abstract Argumentation and referred to as AA-CBR, for problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. In addition, we study properties of explanations in AA-CBR and define a new notion of lean explanations that utilize solely relevant cases. Both forms of explanations can be seen as dialogical processes between a proponent and an opponent, with the burden of proof falling on the proponent

Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future

Demo: Making Plans Scrutable with Argumentation and Natural Language Generation.

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Abduction and Dialogical Proof in Argumentation and Logic Programming

We develop a model of abduction in abstract argumentation, where changes to an argumentation framework act as hypotheses to explain the support of an observation. We present dialogical proof theories for the main decision problems (i.e., finding hypothe- ses that explain skeptical/credulous support) and we show that our model can be instantiated on the basis of abductive logic programs.Comment: Appears in the Proceedings of the 15th International Workshop on Non-Monotonic Reasoning (NMR 2014

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation