In Value-based Argumentation Frameworks (VAFs), values are ascribed to abstract arguments and ordered one to another to reflect an audience’s preferences. An attack of one argument on another is successful only if the audience does not prefer the value of the attacked argument to the value of the attacking argument. Audiences can disagree about admissible arguments relative to their value preferences. Complementary to VAFs, this paper presents a novel integration of Value-Based Reasoning Frameworks (VBFs) with instantiated argumentation, specifically we focus on the structured argumentation approach of ASPIC+. Agents associate literals with social values and weight of values; together, these are used to filter the literals compatible with their values. Such a set of literals is used to construct agent-relative ASPIC+ knowledge bases, agent-relative instantiated arguments, and argumentation frameworks (AFs). Agents can attack one another’s arguments. VAF and VBF present complementary perspectives on values on arguments. VBF contributes a new, formal, articulated view of agreement and disagreement amongst agents, which is grounded in their values. In addition, VBF helps us understand how different agents choose what to argue from out of a pool of common resources.
In this study, we introduce KIALOPRIME, a novel large-scale dataset comprising 5,687 argument discussion graphs with a total of 1,088,801 of supporting, attacking, and neutral argument relations, derived from the structured debates of the online discussion platform Kialo.com. This dataset facilitates in-depth analysis of argument structures and the dynamics of discourse, serving as a substantial resource for computational argumentation research. We explore argument inference through traditional sequence classification and a modern generative reasoning based approach, employing an open-source mixture of experts LLM to interpret and enrich each argument pair with high-quality synthetic elaborations about the argumentative interaction. We achieve baseline results of F1 .899 and .840 within discussions and F1 .908 and .840 across discussions for the argument relation and elaboration classification models, respectively. While the elaboration-based model scores slightly lower on the classification task, we highlight areas of improvement to better capture the hidden complexities of argumentative text. These initial findings are promising as they not only establish robust benchmarks for future studies but also demonstrate the potential for using generative reasoning to provide a more insightful analysis of argument relations.
Most existing computational tools for assumption-based argumentation (ABA) focus on so-called flat frameworks, disregarding the more general case. Here, we study an instantiation-based approach for reasoning in possibly non-flat ABA. For complete-based semantics, an approach of this kind was recently introduced, based on a semantics-preserving translation between ABA and bipolar argumentation frameworks (BAFs). Admissible semantics, however, require us to consider an extension of BAFs which also makes use of premises of arguments (pBAFs). We explore basic properties of pBAFs which we require as a theoretical underpinning for our proposed instantiation-based solver for non-flat ABA under admissible semantics. As our empirical evaluation shows, depending on the ABA instances, the instantiation-based solver is competitive against an ASP-based approach implemented in the style of state-of-the-art solvers for hard argumentation problems.
We propose a new layered visualization in PyArg for grounded labelings of abstract argumentation frameworks. Argument nodes are colored according to their label (IN, OUT, or UNDEC) and have a new length annotation, which is derived from provenance subgraphs. New edge annotations explain an attack-edge’s role in determining the value (label) of nodes in an argumentation framework.
In formal argumentation theory, multiple argumentation-based explanation methods have been formulated based on ideas from social and cognitive science. However, these have not yet been empirically validated. One such idea is that information in an explanation needs to be related; in argumentation-based explanations, this has been captured as there being an attack path between arguments. This study describes and empirically validates two types of relatedness, related admissibility and directly related admissibility. This was done by instructing participants to select arguments from an argumentation framework to explain another argument in this framework. These explanations selected by the participants were compared to argumentation-based explanations that use relatedness. We found that both forms of relatedness are cognitively plausible. This gives insight into how argumentation theory can be applied in the real world to provide explanations.
Dialogue protocols define how a dialogue may proceed and the moves its participants can make within it. The scope of this work covers protocol switching, and addresses the current gap in the area of illicit protocol switches in dialogue. Over the course of a dialogue the participants’ goals and strategies may change in response to the other participants within the dialogue. Enabling agents to switch between protocols gives them the flexibility to address these changes and make use of them. This paper introduces protocol switching using Dialogue as a Service (DaaS), a platform for building multi-agent dialogue systems. DaaS can be used to create a wide range of multi-agent dialogue systems due to its few restrictions and inherent flexibility, which is illustrated through the use of two examples from the literature. Protocol switches can be both licit and illicit; however, current research has only focused on implementing licit protocol switches. An approach to facilitating and managing illicit protocol switches is demonstrated herein.
This paper develops a measure of the influence of individual arguments in abstract argumentation frameworks. By applying ideas from power indices in coalitional game theory, the proposed measure—called admissibility impact value—quantifies the impact that individual arguments have on the set of admissible extensions of a given argumentation framework. It improves on existing impact measures in that it is more fine-grained and sensitive to small differences in the attack relations of argumentation frameworks. Special consideration is given to well-founded frameworks, where the improvements are particularly pronounced.
In this paper, we introduce a novel Argument Mining task based on the existing task of Argument Structure Parsing (ASP). Our new task, which we call ASG Parsing, is the task of generating Argument Summary Graphs (ASGs) from dialogical argumentative text. We release a dataset containing ASGs, a type of graphical summary for argumentative dialogues, in which the nodes are summaries of statements and the edges are the argumentative relations between them (support or attack). We approach the problem with two different LLM-based solutions: (a) a pipeline system involving two models separately fine-tuned for summarisation and stance detection; and (b) an end-to-end system based on the TANL (Translation between Augmented Natural Languages) framework [1]. We show that the TANL approach outperforms the pipeline approach across the board. We also show that, for all systems, performance degrades as the depth of the graphs increases.
Incomplete argumentation frameworks (IAFs) are abstract argumentation frameworks that encode qualitative uncertainty by distinguishing between certain and uncertain arguments and attacks. In a completion of an IAF, each uncertain argument or attack is either added (made certain) or removed. Given a completion, the acceptability of an argument is determined by its justification status. For arguments in an IAF that do not have the same justification status in each completion, it is interesting to study which uncertain arguments and attacks are relevant, in the sense that adding or removing them can lead to a different justification status. We propose algorithms based on Answer Set Programming for enumerating relevant arguments and attacks under grounded and complete semantics.
An important open challenge in the area of computational argumentation is the automatic reconstruction of natural language enthymemes. Such argumentative figures are commonly used in natural language human discourse to improve the naturalness and efficiency of speech. They also represent a major challenge when developing computational argumentation systems that need to work with natural language data, since enthymemes bring irregularity to the representations proposed in classical models of argumentation. In this paper, we propose a new framework based on the theory of argumentation schemes aimed at automatically reconstructing natural language enthymemes. The proposed framework consists of a two-module pipeline: (i) scheme classification, and (ii) enthymeme reconstruction. We validate the proposed framework by comparing its performance to a baseline pipeline that does not take the argumentation scheme theory into account. We evaluate the framework by analysing the validity of the complete reconstructed arguments, establishing a new set of baselines that can be used as reference for future work in this direction.
