Advanced Algorithms for Abstract Dialectical Frameworks based on Complexity Analysis of Subclasses and SAT Solving

dialectical frameworks (ADFs) constitute one of the most powerful formalisms in abstract argumentation. Their high computational complexity poses, however, certain challenges when designing efficient systems. In this paper, we tackle this issue by (i) analyzing the complexity of ADFs under structural restrictions, (ii) presenting novel algorithms which make use of these insights, and (iii) implementing these algorithms via (multiple) calls to SAT solvers. An empirical evaluation of the resulting implementation on ADF benchmarks generated from ICCMA competitions shows that our solver is able to outperform state-of-the-art ADF systems. (c) 2022 The Author(s). Published by Elsevier B.V.Peer reviewe

Investigating subclasses of abstract dialectical frameworks

Dialectical frameworks (ADFs) are generalizations of Dung argumentation frameworks where arbitrary relationships among arguments can be formalized. This additional expressibility comes with the price of higher computational complexity, thus an understanding of potentially easier subclasses is essential. Compared to Dung argumentation frameworks, where several subclasses such as acyclic and symmetric frameworks are well understood, there has been no in-depth analysis for ADFs in such direction yet (with the notable exception of bipolar ADFs). In this work, we introduce certain subclasses of ADFs and investigate their properties. In particular, we show that for acyclic ADFs, the different semantics coincide. On the other hand, we show that the concept of symmetry is less powerful for ADFs and further restrictions are required to achieve results that are similar to the known ones for Dung's frameworks. A particular such subclass (support-free symmetric ADFs) turns out to be closely related to argumentation frameworks with collective attacks (SETAFs); we investigate this relation in detail and obtain as a by-product that even for SETAFs symmetry is less powerful than for AFs. We also discuss the role of odd-length cycles in the subclasses we have introduced. Finally, we analyse the expressiveness of the ADF subclasses we introduce in terms of signatures

Joint attacks and accrual in argumentation frameworks

While modelling arguments, it is often useful to represent joint attacks, i.e., cases where multiple arguments jointly attack another (note that this is different from the case where multiple arguments attack another in isolation). Based on this remark, the notion of joint attacks has been proposed as a useful extension of classical Abstract Argumentation Frameworks, and has been shown to constitute a genuine extension in terms of expressive power. In this chapter, we review various works considering the notion of joint attacks from various perspectives, including abstract and structured frameworks. Moreover, we present results detailing the relation among frameworks with joint attacks and classical argumentation frameworks, computational aspects, and applications of joint attacks. Last but not least, we propose a roadmap for future research on the subject, identifying gaps in current research and important research directions.Fil: Bikakis, Antonis. University College London; Estados UnidosFil: Cohen, Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Dvoák, Wolfgang. Technische Universitat Wien; AustriaFil: Flouris, Giorgos. Foundation for Research and Technology; GreciaFil: Parsons, Simon. University of Lincoln; Reino Unid

Online Handbook of Argumentation for AI: Volume 1

This volume contains revised versions of the papers selected for the first volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.Comment: editor: Federico Castagna and Francesca Mosca and Jack Mumford and Stefan Sarkadi and Andreas Xydi

Integrating ontologies and argumentation for decision-making in breast cancer

This thesis describes some of the problems in providing care for patients with breast cancer. These are then used to motivate the development of an extension to an existing theory of argumentation, which I call the Ontology-based Argumentation Formalism (OAF). The work is assessed in both theoretical and empirical ways. From a clinical perspective, there is a problem with the provision of care. Numerous reports have noted the failure to provide uniformly high quality care, as well as the number of deaths caused by medical care. The medical profession has responded in various ways, but one of these has been the development of Decision Support Systems (DSS). The evidence for the effectiveness of such systems is mixed, and the technical basis of such systems remains open to debate. However, one basis that has been used is argumentation. An important aspect of clinical practice is the use of the evidence from clinical trials, but these trials are based on the results in defined groups of patients. Thus when we use the results of clinical trials to reason about treatments, there are two forms of information we are interested in - the evidence from trials and the relationships between groups of patients and treatments. The relational information can be captured in an ontology about the groups of patients and treatments, and the information from the trials captured as a set of defeasible rules. OAF is an extension of an existing argumentation system, and provides the basis for an argumentation-based Knowledge Representation system which could serve as the basis for future DSS. In OAF, the ontology provides a repository of facts, both asserted and inferred on the basis of formulae in the ontology, as well as defining the language of the defeasible rules. The defeasible rules are used in a process of defeasible reasoning, where monotonic consistent chains of reasoning are used to draw plausible conclusions. This defeasible reasoning is used to generate arguments and counter-arguments. Conflict between arguments is defined in terms of inconsistent formulae in the ontology, and by using existing proposals for ontology languages we are able to make use of existing proposals and technologies for ontological reasoning. There are three substantial areas of novel work: I develop an extension to an existing argumentation formalism, and prove some simple properties of the formalism. I also provide a novel formalism of the practical syllogism and related hypothetical reasoning, and compare my approach to two other proposals in the literature. I conclude with a substantial case study based on a breast cancer guideline, and in order to do so I describe a methodology for comparing formal and informal arguments, and use the results of this to discuss the strengths and weaknesses of OAF. In order to develop the case study, I provide a prototype implementation. The prototype uses a novel incremental algorithm to construct arguments and I give soundness, completeness and time-complexity results. The final chapter of the thesis discusses some general lessons from the development of OAF and gives ideas for future work

Updating belief in arguments in epistemic graphs

Epistemic graphs are a recent generalization of epistemic probabilistic argumentation. Relations between arguments can be supporting, attacking, as well as neither supporting nor attacking. These interdependencies are represented by epistemic constraints, and the semantics of epistemic graphs are given in terms of probability distributions satisfying these constraints. We investigate the behaviour of epistemic graphs in a dynamic setting where a given distribution can be updated once new constraints are presented. Our focus is on update methods that minimize the change in probabilistic beliefs. We show that all methods satisfy basic commonsense postulates, identify fragments of the epistemic constraint language that guarantee the existence of well-defined solutions, and explain how the problems that arise in more expressive fragments can be treated either automatically or by user support. We demonstrate the usefulness of our proposal by considering its application in computational persuasion