322 research outputs found

    Reasoning over Assumption-Based Argumentation Frameworks via Answer Set Programming

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    Formal argumentation is a vibrant research area within artificial intelligence, in particular in knowledge representation and reasoning. Computational models of argumentation are divided into abstract and structured formalisms. Since its introduction in 1995, abstract argumentation, where the structure of arguments is abstracted away, has been much studied and applied. Structured argumentation formalisms, on the other hand, contain the explicit derivation of arguments. This is motivated by the importance of the construction of arguments in the application of argumentation formalisms, but also makes structured formalisms conceptually and often computationally more complex than abstract argumentation. The focus of this work is on assumption-based argumentation (ABA), a major structured formalism. Specifically we address the relative lack of efficient computational tools for reasoning in ABA compared to abstract argumentation. The computational efficiency of ABA reasoning systems has been markedly lower than the systems for abstract argumentation. In this thesis we introduce a declarative approach to reasoning in ABA via answer set programming (ASP), drawing inspiration from existing tools for abstract argumentation. In addition, we consider ABA+, a generalization of ABA that incorporates preferences into the formalism. The complexity of reasoning in ABA+ is higher than in ABA for most problems. We are able to extend our declarative approach to some ABA+ reasoning problems. We show empirically that our approach vastly outperforms previous reasoning systems for ABA and ABA+

    EMIL: Extracting Meaning from Inconsistent Language

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    Developments in formal and computational theories of argumentation reason with inconsistency. Developments in Computational Linguistics extract arguments from large textual corpora. Both developments head in the direction of automated processing and reasoning with inconsistent, linguistic knowledge so as to explain and justify arguments in a humanly accessible form. Yet, there is a gap between the coarse-grained, semi-structured knowledge-bases of computational theories of argumentation and fine-grained, highly-structured inferences from knowledge-bases derived from natural language. We identify several subproblems which must be addressed in order to bridge the gap. We provide a direct semantics for argumentation. It has attractive properties in terms of expressivity and complexity, enables reasoning by cases, and can be more highly structured. For language processing, we work with an existing controlled natural language (CNL), which interfaces with our computational theory of argumentation; the tool processes natural language input, translates them into a form for automated inference engines, outputs argument extensions, then generates natural language statements. The key novel adaptation incorporates the defeasible expression ‘it is usual that’. This is an important, albeit incremental, step to incorporate linguistic expressions of defeasibility. Overall, the novel contribution of the paper is an integrated, end-to-end argumentation system which bridges between automated defeasible reasoning and a natural language interface. Specific novel contributions are the theory of ‘direct semantics’, motivations for our theory, results with respect to the direct semantics, an implementation, experimental results, the tie between the formalisation and the CNL, the introduction into a CNL of a natural language expression of defeasibility, and an ‘engineering’ approach to fine-grained argument analysis

    An Answer Set Programming Approach to Argumentative Reasoning in the ASPIC+ Framework

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

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    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
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