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

A generalised framework for dispute derivations in assumption-based argumentation

AbstractAssumption-based argumentation is a general-purpose argumentation framework with well-understood theoretical foundations and viable computational mechanisms (in the form of dispute derivations), as well as several applications. However, the existing computational mechanisms have several limitations, hindering their deployment in practice: (i) they are defined in terms of implicit parameters, that nonetheless need to be instantiated at implementation time; (ii) they are variations (for computing different semantics) of one another, but still require different implementation efforts; (iii) they reduce the problem of computing arguments to the problem of computing assumptions supporting these arguments, even though applications of argumentation require a justification of claims in terms of explicit arguments and attacks between them.In this context, the contribution of this paper is two-fold. Firstly, we provide a unified view of the existing (GB-, AB- and IB-)dispute derivations (for computation under the grounded, admissible and ideal semantics, respectively), by obtaining them as special instances of a single notion of X-dispute derivations that, in addition, renders explicit the implicit parameters in the original dispute derivations. Thus, X-dispute derivations address issues (i) and (ii). Secondly, we define structured X-dispute derivations, extending X-dispute derivations by computing explicitly the underlying arguments and attacks, in addition to assumptions. Thus, structured X-dispute derivations also address issue (iii). We prove soundness and completeness results for appropriate instances of (structured) X-dispute derivations, w.r.t. the grounded, admissible and ideal semantics, thus laying the necessary theoretical foundations for deployability thereof

From Structured to Abstract Argumentation : Assumption-Based Acceptance via AF Reasoning

Peer reviewe

Justifying Answer Sets using Argumentation

An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal is or is not contained in a given answer set by defining two justification methods, both of which make use of the correspondence between answer sets of a logic program and stable extensions of the Assumption-Based Argumentation (ABA) framework constructed from the same logic program. Attack Trees justify a literal in argumentation-theoretic terms, i.e. using arguments and attacks between them, whereas ABA-Based Answer Set Justifications express the same justification structure in logic programming terms, that is using literals and their relationships. Interestingly, an ABA-Based Answer Set Justification corresponds to an admissible fragment of the answer set in question, and an Attack Tree corresponds to an admissible fragment of the stable extension corresponding to this answer set.Comment: This article has been accepted for publication in Theory and Practice of Logic Programmin

Reasoning over Assumption-Based Argumentation Frameworks via Direct Answer Set Programming Encodings

Peer reviewe

Developments in abstract and assumption-based argumentation and their application in logic programming

Logic Programming (LP) and Argumentation are two paradigms for knowledge representation and reasoning under incomplete information. Even though the two paradigms share common features, they constitute mostly separate areas of research. In this thesis, we present novel developments in Argumentation, in particular in Assumption-Based Argumentation (ABA) and Abstract Argumentation (AA), and show how they can 1) extend the understanding of the relationship between the two paradigms and 2) provide solutions to problematic reasoning outcomes in LP. More precisely, we introduce assumption labellings as a novel way to express the semantics of ABA and prove a more straightforward relationship with LP semantics than found in previous work. Building upon these correspondence results, we apply methods for argument construction and conflict detection from ABA, and for conflict resolution from AA, to construct justifications of unexpected or unexplained LP solutions under the answer set semantics. We furthermore characterise reasons for the non-existence of stable semantics in AA and apply these findings to characterise different scenarios in which the computation of meaningful solutions in LP under the answer set semantics fails.Open Acces

Arg-tuProlog: A tuProlog-based argumentation framework

Over the last decades, argumentation has become increasingly central as a frontier research within artificial intelligence (AI), especially around the notions of interpretability and explainability, which are more and more required within AI applications. In this paper we present the first prototype of Arg-tuProlog, a logic-based argumentation tool built on top of the tuProlog system. In particular, Arg-tuProlog enables defeasible reasoning and argumentation, and deals with priorities over rules. It also includes a formal method for dealing with burden of proof (burden of persuasion). Being lightweight and compliant to the requirements for micro-intelligence, Arg-tuProlog is perfectly suited for injecting argumentation into distributed pervasive systems

Declarative Algorithms and Complexity Results for Assumption-Based Argumentation

Peer reviewe

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+