A Comparative Study of Ranking-based Semantics for Abstract Argumentation

Argumentation is a process of evaluating and comparing a set of arguments. A way to compare them consists in using a ranking-based semantics which rank-order arguments from the most to the least acceptable ones. Recently, a number of such semantics have been proposed independently, often associated with some desirable properties. However, there is no comparative study which takes a broader perspective. This is what we propose in this work. We provide a general comparison of all these semantics with respect to the proposed properties. That allows to underline the differences of behavior between the existing semantics.Comment: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-2016), Feb 2016, Phoenix, United State

Argumentation Ranking Semantics based on Propagation

International audienceArgumentation is based on the exchange and the evaluation of interacting arguments. Unlike Dung's theory where arguments are either accepted or rejected, ranking-based semantics rank-order arguments from the most to the least acceptable ones. We propose in this work six new ranking-based semantics. We argue that, contrarily to existing ranking semantics in the literature, that focus on evaluating attacks and defenses only, it is reasonable to give a prominent role to non-attacked arguments, as it is the case in standard Dung's semantics. Our six semantics are based on the propagation of the weight of each argument to its neighbors, where the weight of non-attacked arguments is greater than the attacked ones

Interpretability of Gradual Semantics in Abstract Argumentation

International audiencergumentation, in the field of Artificial Intelligence, is a for-malism allowing to reason with contradictory information as well as tomodel an exchange of arguments between one or several agents. For thispurpose, many semantics have been defined with, amongst them, grad-ual semantics aiming to assign an acceptability degree to each argument.Although the number of these semantics continues to increase, there iscurrently no method allowing to explain the results returned by thesesemantics. In this paper, we study the interpretability of these seman-tics by measuring, for each argument, the impact of the other argumentson its acceptability degree. We define a new property and show that thescore of an argument returned by a gradual semantics which satisfies thisproperty can also be computed by aggregating the impact of the otherarguments on it. This result allows to provide, for each argument in anargumentation framework, a ranking between arguments from the most to the least impacting ones w.r.t a given gradual semantic

Comparing and Extending the Use of Defeasible Argumentation with Quantitative Data in Real-World Contexts

Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new evidence, offering some desirable flexibility when dealing with uncertainty. Among possible options, knowledge-base, non-monotonic reasoning approaches have seen their use being increased in practice. Nonetheless, only a limited number of works and researchers have performed any sort of comparison among them. This research article focuses on evaluating the inferential capacity of defeasible argumentation, a formalism particularly envisioned for modelling non-monotonic reasoning. In addition to this, fuzzy reasoning and expert systems, extended for handling non-monotonicity of reasoning, are selected and employed as baselines, due to their vast and accepted use within the AI community. Computational trust was selected as the domain of application of such models. Trust is an ill-defined construct, hence, reasoning applied to the inference of trust can be seen as non-monotonic. Inference models were designed to assign trust scalars to editors of the Wikipedia project. Scalars assigned to recognised trustworthy editors provided the basis for the analysis of the models’ inferential capacity according to evaluation metrics from the domain of computational trust. In particular, argument-based models demonstrated more robustness than those built upon the baselines despite the knowledge bases or datasets employed. This study contributes to the body of knowledge through the exploitation of defeasible argumentation and its comparison to similar approaches. It provides publicly implementations for the designed models of inference, which might be a useful aid to scholars interested in performing non-monotonic reasoning activities. It adds to previous works, empirically enhancing the generalisability of defeasible argumentation as a compelling approach to reason with quantitative data and uncertain knowledge

Ranking Semantics Based on Subgraphs Analysis

An abstract argumentation framework [15] consists of a direct graph where nodes represent arguments and arrows represent an attack relation among arguments. A semantics is used to evaluate arguments’ acceptability. In the labelling approach [7], this evaluation is done by assigning to each argument a label in, out or undec, meaning that the argument is considered consistently acceptable, non-acceptable or undecided (i.e. no decision can be taken on arguments’ acceptability)