Ensuring reference independence and cautious monotony in abstract argumentation

In the research on dynamics of argumentation frameworks (AFs), the enforcement problem deals with changing an AF for the purpose of ensuring that a certain set of desired arguments becomes (part of) an extension. In this paper we focus on expansions of an AF where solely the addition of new arguments and attacks is allowed and the original framework remains unchanged. Existing results about the enforcement problem are all sufficient conditions, and we argue that necessary and sufficient conditions are essential concerning the solvability of the enforcement problem. A suite of jointly necessary and sufficient conditions are identified for the enforcement problem under normal expansion. These conditions are presented from multiple perspectives including the sub-strict versus non-strict problem, constructive versus eliminative conditions and arbitrary AFs versus even(odd)-length cycle free AFs, determined by the nature of various argumentation semantics. Such necessary and sufficient conditions are of the essence to the enforcement problem by defining requirements that are both minimum and absolute, which can be used to determine when the enforcement problem is (un)solvable. We further infer solely sufficient and solely necessary conditions based on the partial order relation (w.r.t. set-inclusion) among various types of AF extensions.

Claim-augmented argumentation frameworks (CAFs) constitute a generic formalism for conflict resolution of conclusion-oriented problems in argumentation. CAFs extend Dung argumentation frameworks (AFs) by assigning a claim to each argument. So far, semantics for CAFs are defined with respect to the underlying AF by interpreting the extensions of the respective AF semantics in terms of the claims of the accepted arguments; we refer to them as inherited semantics of CAFs.<br>A central concept of many argumentation semantics is maximization, which can be done with respect to arguments as in preferred semantics, or with respect to the range as in semi-stable semantics. However, common instantiations of argumentation frameworks require maximality on the claim-level and inherited semantics often fail to provide maximal claim-sets even if the underlying AF semantics yields maximal argument sets. To address this issue, we investigate a different approach and introduce claim-level semantics (cl-semantics) for CAFs where maximization is performed on the claim-level. We compare these two approaches for five prominent semantics (preferred, naive, stable, semi-stable, and stage) and relate in total eleven CAF semantics to each other. Moreover, we show that for a certain subclass of CAFs, namely well-formed CAFs, the different versions of preferred and stable semantics coincide, which is not the case for the remaining semantics. We furthermore investigate a recently established translation between well-formed CAFs and SETAFs and show that, in contrast to the inherited naive, semi-stable and stage semantics, the cl-semantics correspond to the respective SETAF semantics. Finally, we investigate the expressiveness of the considered semantics in terms of their signatures.

The changing of arguments and their attack relation is an intrinsic property of a variety of argumentation systems. So, it is very important to efficiently figure out how the status of arguments in a system evolves when the system is updated. However, unlike other areas of argumentation that have been deeply explored, such as argumentation semantics, proof theories, and algorithms, etc., dynamics of argumentation systems has been comparatively neglected. In this paper, we introduce a general theory (called a division-based method) to cope with this problem based on a new concept: the division of an argumentation framework. When an argumentation framework is updated, it is divided into three parts: an unaffected, an affected, and a conditioning part. The status of arguments in the unaffected sub-framework remains unchanged, while the status of the affected arguments is computed in a special argumentation framework (called a conditioned argumentation framework, or briefly CAF) that is composed of an affected part and a conditioning part. We have proved that under a certain semantics that satisfies the directionality criterion (complete, preferred, ideal, or grounded semantics), the extensions of the updated framework are equal to the result of a combination of the extensions of an unaffected sub-framework and sets of the extensions of a set of assigned CAFs. The theory shows that the complexity of computing the dynamics of argumentation will decrease to a lesser or greater extent, depending on the types of argumentation semantics, the topologies of argumentation frameworks, and the number of affected arguments with respect to an addition or a deletion. As a result, this theory is expected to be very useful in various kinds of argumentation systems where arguments and attacks are dynamics, due to the changing of underlying knowledge and information.

Dynamics of argumentation systems: A division-based method

We propose a set of novel ranking-based semantics based on a measure of the sensitivity of each argument in an abstract argumentation framework. The sensitivity index is an indicator of how sensitive the label assigned to an argument by an argumentation semantics is, and it is derived from the topology of the graph via a subgraphs analysis coupled with the postulates of the chosen semantics. Using the total rank on arguments induced by such indicator, we propose two ranking-based semantics. We compare the behaviour of our semantics with recent proposals and a widespread set of properties identified in literature. A key feature of our semantics is that the attack relation between arguments keeps the same meaning as found in Dung's abstract semantics. By still relying on Dung's semantics we can soundly deal with any graph configuration, minimize the addition of ad-hoc postulates and provide a clear interpretation of the ranking of arguments.

We present a novel automated approach for the computation and verification of preferences in an abstract argumentation system. Various argumentation semantics have been developed for identifying acceptable sets of arguments, however, there is a lack of explanatory justification for their acceptability based on preferences. We present an algorithm which takes an abstract argumentation framework and a single extension (conflict-free set of arguments) as input, and outputs preference relations that explain why a set of arguments are acceptable as opposed to their attackers. We also present an algorithm to verify that the output preferences when used with the given argumentation framework induce the input extension.

We introduce Incomplete Bipolar Argumentation Frameworks (iBAFs), the extension of Dung’s Abstract Argumentation Frameworks (AAFs) allowing the simultaneous presence of supports (borrowed from BAFs – Bipolar AAFs) and of uncertain elements of the argumentation graph (borrowed from iAAFs – incomplete AAFs). We investigate the computational complexity of verification problem (under the possible perspective) and the acceptance problem, by studying its sensitivity to the semantics of supports and the semantics of extensions. On the one hand, we show that adding supports on top of incompleteness does not affect the complexity of the acceptance. On the other hand, surprisingly, we show that the joint use of bipolarity and incompleteness has a deep impact on the complexity of the verification: for the semantics under which the verification over AAFs is polynomial-time solvable, although moving from AAFs to BAFs or to iAAFs does not change the complexity, the complexity of the verification over iBAFs may increase up to NP-complete.

We study a number of properties concerning the behaviour of semantics for Dung style abstract argumentation when the argumentation framework changes. The properties are concerned with how the evaluation of an argumentation framework changes if an attack between two arguments is added or removed. The results provide insight into the behaviour of these semantics in a dynamic context.

We endow Brewka's prioritised default logic (PDL) with argumentation semantics using the ASPIC+M framework for structured argumentation. We prove that the conclusions of the justified arguments correspond to the prioritised default extensions in a normatively rational manner. Argumentation semantics for PDL will allow for the application of argument game proof theories to the process of inference in PDL, making the reasons for accepting a conclusion transparent and the inference process more intuitive. This also opens up the possibility for argumentation-based distributed reasoning and communication amongst agents with PDL representations of mental attitudes.

Intelligence analysis is not a passive process, but proactive and reactive. Analysts have the opportunity to request collection of further intelligence to improve the analysis. The identification and selection of collection opportunities is a human led process requiring an understanding of the value of a collection to be balanced against the cost and risks of making the collection. Argumentation frameworks are a mathematical formalization to describe arguments and conflicts between such arguments. Argumentation frameworks can be used to represent intelligence analyses, with observations, hypotheses and assumptions represented as arguments and conflicts between these represented by attacks. A value of information can be defined over an argumentation framework based on the change in the result of evaluation of the framework, according to argumentation semantics, when arguments are added or removed. When accompanied by a representation of collection opportunities, the value of information can be used to assess which collection opportunity is most beneficial. In this paper we define a framework for calculating the net benefit over a closed set of potential collection opportunities. This framework is demonstrated against an example intelligence analysis on maritime situational awareness scenario.

A fuzzy multipreference semantics has been recently proposed for weighted conditional knowledge bases, and used to develop a logical semantics for Multilayer Perceptrons, by regarding a deep neural network (after training) as a weighted conditional knowledge base. This semantics, in its different variants, suggests some gradual argumentation semantics, which are related to the family of the gradual semantics studied by Amgoud and Doder. The relationships between weighted conditional knowledge bases and MLPs extend to the proposed gradual semantics to capture the stationary states of MPs, in agreement with previous results on the relationship between argumentation frameworks and neural networks. The paper also suggests a simple way to extend the proposed semantics to deal attacks/supports by a boolean combination of arguments, based on the fuzzy semantics of weighted conditionals, as well as an approach for defeasible reasoning over a weighted argumentation graph, building on the proposed gradual semantics.

In the last few years, argumentation frameworks have been successfully applied to multi agent systems. Recently, argumentation has been used to provide a framework for reasoning about coalition formation. At the same time alternating-time temporal logic has been used to reason about the behavior and abilities of coalitions of agents. However, ATL operators account only for the existence of successful strategies of coalitions. They do not consider whether coalitions can be actually formed.This paper is an attempt to combine both frameworks and to develop a logic through which we can reason at the same time (1) about abilities of coalitions of agents and (2) about the formation of coalitions. We provide a formal extension of ATL, ATLc, in which the actual computation of the coalition is modelled in terms of argumentation semantics. We show that ATLc's proof theory can be understood as a natural extension of the model checking procedure used in ATL.

Based on Dung’s argumentation framework, using Dung’s extended argumentation semantics, importing reasoning argument and fact argument, design and implementing Jess-based argumentative E-commerce system, the system has important significance and high reference value for studying E-commerce argumentative negotiation systems.

In the last few years, argumentation frameworks have been successfully applied to multi agent systems. Recently, argumentation has been used to provide a framework for reasoning about coalition formation. At the same time alternating-time temporal logic has been used to reason about the behavior and abilities of coalitions of agents. However, ATL operators account only for the existence of successful strategies of coalitions. They do not consider whether coalitions can be actually formed. This paper is an attempt to combine both frameworks and to develop a logic through which we can reason at the same time (1) about abilities of coalitions of agents and (2) about the formation of coalitions. We provide a formal extension of ATL, ATLc, in which the actual computation of the coalition is modelled in terms of argumentation semantics. We show that ATLc's proof theory can be understood as a natural extension of the model checking procedure used in ATL.

The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.