In the context of Dung's theory of abstract argumentation frameworks, the recently introduced resolution-based grounded semantics features the unique property of fully complying with a set of general requirements, only partially satisfied by previous literature proposals. This paper contributes to the investigation of resolution-based grounded semantics by analyzing its computational properties with reference to a standard set of decision problems for abstract argumentation semantics: (a) checking the property of being an extension for a set of arguments; (b) checking agreement with traditional grounded semantics; (c) checking the existence of a non-empty extension; (d) checking credulous acceptance of an argument; (e) checking skeptical acceptance of an argument. It is shown that problems (a)-(c) admit polynomial time decision processes, while (d) is NP-complete and (e) coNP-complete.

Argumentation is a reasoning model based on the construction and evaluation of arguments. Dung has proposed an abstract argumentation framework in which arguments are assumed to have the same strength. This assumption is unfortunately not realistic. Consequently, three main extensions of the framework have been proposed in the literature. The basic idea is that if an argument is stronger than its attacker, the attack fails. The aim of the paper is twofold: First, it shows that the three extensions of Dung framework may lead to unintended results. Second, it proposes a new approach that takes into account the strengths of arguments, and that ensures sound results. We start by presenting two minimal requirements that any preference-based argumentation framework should satisfy, namely the conflict-freeness of arguments extensions and the generalization of Dung’s framework. Inspired from works on handling inconsistency in knowledge bases, the proposed approach defines a binary relation on the powerset of arguments. The maximal elements of this relation represent the extensions of the new framework.

This paper discusses a process of argumentation. We propose an algorithm for dynamic treatment of argumentation in which all lines of argumentation are executed in succession, and the agent's knowledge base can change during argumentation. We show that there exists a case in which an agent dynamically loses argumentation that would be considered won by a static analysis. We also show that the algorithm terminates, and describe acceptable arguments that are obtained after the argumentation.

Given an argumentation framework AF, we introduce a mapping function that constructs a disjunctive logic program P, such that the preferred extensions of AF correspond to the stable models of P, after intersecting each stable model with the relevant atoms. The given mapping function is of polynomial size w.r.t. AF. In particular, we identify that there is a direct relationship between the minimal models of a propositional formula and the preferred extensions of an argumentation framework by working on representing the defeated arguments. Then we show how to infer the preferred extensions of an argumentation framework by using UNSAT algorithms and disjunctive stable model solvers. The relevance of this result is that we define a direct relationship between one of the most satisfactory argumentation semantics and one of the most successful approach of non-monotonic reasoning i.e., logic programming with the stable model semantics.

Argumentation is based on the exchange and valuation of interacting arguments, followed by the selection of the most acceptable of them (for example, in order to take a decision, to make a choice). Starting from the framework proposed by Dung in 1995, our purpose is to introduce 'graduality' in the selection of the best arguments, i.e., to be able to partition the set of the arguments in more than the two usual subsets of 'selected' and 'non-selected' arguments in order to represent different levels of selection. Our basic idea is that an argument is all the more acceptable if it can be preferred to its attackers. First, we discuss general principles underlying a 'gradual' valuation of arguments based on their interactions. Following these principles, we define several valuation models for an abstract argumentation system. Then, we introduce 'graduality' in the concept of acceptability of arguments. We propose new acceptability classes and a refinement of existing classes taking advantage of an available 'gradual' valuation.

In a complex, dynamic multiagent setting, coherent team actions are often jeopardized by conflicts in agents' beliefs, plans, and actions. Despite the considerable progress in teamwork research, the challenge of intrateam conflict resolution has remained largely unaddressed. This article presents CONSA, a system we are developing to resolve conflicts using argumentation-based negotiations. CONSA focuses on exploiting the benefits of argumentation in a team setting. Thus, CONSA casts conflict resolution as a team problem, so that the recent advances in teamwork can be brought to bear during conflict resolution to improve argumentation flexibility. Furthermore, because teamwork conflicts sometimes involve past teamwork, teamwork models can be exploited to provide agents with reusable argumentation knowledge. Additionally, CONSA also includes argumentation strategies geared toward benefiting the team, rather than the individual, and techniques to reduce argumentation overhead.

Moore's technique employs a conventional

This article is a summary of the Workshop on Explanation held during the 1988 National Conference on Artificial Intelligence in St. Paul, Minnesota. The purpose of the workshop was to identify key research issues in the rapidly emerging area of expert system explanation.