Reasoning over Assumption-Based Argumentation Frameworks via Answer Set Programming

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+

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