An action language-based formalisation of an abstract argumentation framework

• URL: http://arxiv.org/abs/2409.19625v1
• Date: Sun, 29 Sep 2024 09:24:29 GMT
• Title: An action language-based formalisation of an abstract argumentation framework
• Authors: Yann Munro, Camilo Sarmiento, Isabelle Bloch, Gauvain Bourgne, Catherine Pelachaud, Marie-Jeanne Lesot,
• Abstract summary: We propose a new framework for modelling abstract argumentation graphs. By taking the order of enunciation into account, we have the means to deduce a unique outcome for each dialogue.
• Score: 2.6988814189407937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An abstract argumentation framework is a commonly used formalism to provide a static representation of a dialogue. However, the order of enunciation of the arguments in an argumentative dialogue is very important and can affect the outcome of this dialogue. In this paper, we propose a new framework for modelling abstract argumentation graphs, a model that incorporates the order of enunciation of arguments. By taking this order into account, we have the means to deduce a unique outcome for each dialogue, called an extension. We also establish several properties, such as termination and correctness, and discuss two notions of completeness. In particular, we propose a modification of the previous transformation based on a "last enunciated last updated" strategy, which verifies the second form of completeness.

Related papers

• AMERICANO: Argument Generation with Discourse-driven Decomposition and Agent Interaction [25.38899822861742]
  We propose Americano, a novel framework with agent interaction for argument generation. Our approach decomposes the generation process into sequential actions grounded on argumentation theory. Our method outperforms both end-to-end and chain-of-thought prompting methods.
  arXiv  Detail & Related papers  (2023-10-31T10:47:33Z)
• A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
  We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
  arXiv  Detail & Related papers  (2023-10-18T20:18:05Z)
• Semi-Abstract Value-Based Argumentation Framework [0.0]
  Phan Minh Dung proposed abstract argumentation framework, which models argumentation using directed graphs where structureless arguments are the nodes and attacks among the arguments are the edges. This thesis showcases two such extensions -- value-based argumentation framework by Trevor Bench-Capon (2002) and semi-abstract argumentation framework by Esther Anna Corsi and Christian Ferm" The contribution of this thesis is two-fold. Firstly, the new semi-abstract value-based argumentation framework is introduced. This framework maps propositional formulae associated with individual arguments to a set of ordered values.
  arXiv  Detail & Related papers  (2023-09-25T13:10:56Z)
• JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning [53.83063435640911]
  Dialogue policy learning (DPL) is a crucial component of dialogue modelling. We introduce a novel framework, JoTR, to generate flexible dialogue actions. Unlike traditional methods, JoTR formulates a word-level policy that allows for a more dynamic and adaptable dialogue action generation.
  arXiv  Detail & Related papers  (2023-09-01T03:19:53Z)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• Integrating Temporality and Causality into Acyclic Argumentation Frameworks using a Transition System [0.5420492913071214]
  We propose a formal method to rewrite the concepts of acyclic abstract argumentation frameworks into an action language. We establish causal relationships between the enunciation of arguments and their consequences, whether direct or indirect. An Answer Set Programming implementation is also proposed, as well as perspectives towards explanations.
  arXiv  Detail & Related papers  (2023-03-16T10:13:47Z)
• A Formalisation of Abstract Argumentation in Higher-Order Logic [77.34726150561087]
  We present an approach for representing abstract argumentation frameworks based on an encoding into classical higher-order logic. This provides a uniform framework for computer-assisted assessment of abstract argumentation frameworks using interactive and automated reasoning tools.
  arXiv  Detail & Related papers  (2021-10-18T10:45:59Z)
• Controlling Dialogue Generation with Semantic Exemplars [55.460082747572734]
  We present an Exemplar-based Dialogue Generation model, EDGE, that uses the semantic frames present in exemplar responses to guide generation. We show that controlling dialogue generation based on the semantic frames of exemplars, rather than words in the exemplar itself, improves the coherence of generated responses.
  arXiv  Detail & Related papers  (2020-08-20T17:02:37Z)
• AMPERSAND: Argument Mining for PERSuAsive oNline Discussions [41.06165177604387]
  We propose a computational model for argument mining in online persuasive discussion forums. Our approach relies on identifying relations between components of arguments in a discussion thread. Our models obtain significant improvements compared to recent state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-04-30T10:33:40Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.