Necessary and Sufficient Explanations in Abstract Argumentation

• URL: http://arxiv.org/abs/2011.02414v1
• Date: Wed, 4 Nov 2020 17:12:12 GMT
• Title: Necessary and Sufficient Explanations in Abstract Argumentation
• Authors: AnneMarie Borg and Floris Bex
• Abstract summary: We discuss necessary and sufficient explanations for formal argumentation. We study necessity and sufficiency: what (sets of) arguments are necessary or sufficient for the (non-acceptance) of an argument?
• Score: 3.9849889653167208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we discuss necessary and sufficient explanations for formal argumentation - the question whether and why a certain argument can be accepted (or not) under various extension-based semantics. Given a framework with which explanations for argumentation-based conclusions can be derived, we study necessity and sufficiency: what (sets of) arguments are necessary or sufficient for the (non-)acceptance of an argument?

Related papers

• Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation [19.799266797193344]
  Argumentation-based systems often lack explainability while supporting decision-making processes. Counterfactual and semifactual explanations are interpretability techniques. We show that counterfactual and semifactual queries can be encoded in weak-constrained Argumentation Framework.
  arXiv  Detail & Related papers  (2024-05-07T07:27:27Z)
• 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)
• Many-valued Argumentation, Conditionals and a Probabilistic Semantics for Gradual Argumentation [3.9571744700171743]
  We propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics. As a proof of concept, in the finitely-valued case, an Answer set Programming approach is proposed for conditional reasoning. The paper also develops and discusses a probabilistic semantics for gradual argumentation, which builds on the many-valued conditional semantics.
  arXiv  Detail & Related papers  (2022-12-14T22:10:46Z)
• MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure [129.8481568648651]
  We propose a benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Based on the multi-hop chain of reasoning, the explanation form includes three main components. We evaluate the current best models' performance on this new explanation form.
  arXiv  Detail & Related papers  (2022-10-22T16:01:13Z)
• Annotating Implicit Reasoning in Arguments with Causal Links [34.77514899468729]
  We focus on identifying the implicit knowledge in the form of argumentation knowledge. Being inspired by the Argument from Consequences scheme, we propose a semi-structured template to represent such argumentation knowledge. We show how to collect and filter high-quality implicit reasonings via crowdsourcing.
  arXiv  Detail & Related papers  (2021-10-26T13:28:53Z)
• 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)
• Contrastive Explanations for Argumentation-Based Conclusions [5.1398743023989555]
  We discuss contrastive explanations for formal argumentation. We show under which conditions contrastive explanations are meaningful, and how argumentation allows us to make implicit foils explicit.
  arXiv  Detail & Related papers  (2021-07-07T15:00:47Z)
• Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations [70.35379323231241]
  This paper presents a better approach for event extraction by explicitly utilizing the relationships of event arguments. We employ reinforcement learning and incremental learning to extract multiple arguments via a multi-turned, iterative process. Experimental results show that our approach consistently outperforms seven state-of-the-art event extraction methods.
  arXiv  Detail & Related papers  (2021-06-23T13:24:39Z)
• Exploring Discourse Structures for Argument Impact Classification [48.909640432326654]
  This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. We propose DisCOC to inject and fuse the sentence-level structural information with contextualized features derived from large-scale language models.
  arXiv  Detail & Related papers  (2021-06-02T06:49:19Z)
• Thinking About Causation: A Causal Language with Epistemic Operators [58.720142291102135]
  We extend the notion of a causal model with a representation of the state of an agent. On the side of the object language, we add operators to express knowledge and the act of observing new information. We provide a sound and complete axiomatization of the logic, and discuss the relation of this framework to causal team semantics.
  arXiv  Detail & Related papers  (2020-10-30T12:16:45Z)
• Extracting Implicitly Asserted Propositions in Argumentation [8.20413690846954]
  We study methods for extracting propositions implicitly asserted in questions, reported speech, and imperatives in argumentation. Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation.
  arXiv  Detail & Related papers  (2020-10-06T12:03:47Z)

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.