On the Correspondence of Non-flat Assumption-based Argumentation and Logic Programming with Negation as Failure in the Head

• URL: http://arxiv.org/abs/2405.09415v2
• Date: Fri, 24 May 2024 15:25:22 GMT
• Title: On the Correspondence of Non-flat Assumption-based Argumentation and Logic Programming with Negation as Failure in the Head
• Authors: Anna Rapberger, Markus Ulbricht, Francesca Toni,
• Abstract summary: We show a correspondence between non-flat ABA and LPs with negation as failure in their head. We then extend this result to so-called set-stable ABA semantics, originally defined for the fragment of non-flat ABA called bipolar ABA. We showcase how to define set-stable semantics for LPs with negation as failure in their head and show the correspondence to set-stable ABA semantics.
• Score: 20.981256612743145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The relation between (a fragment of) assumption-based argumentation (ABA) and logic programs (LPs) under stable model semantics is well-studied. However, for obtaining this relation, the ABA framework needs to be restricted to being flat, i.e., a fragment where the (defeasible) assumptions can never be entailed, only assumed to be true or false. Here, we remove this restriction and show a correspondence between non-flat ABA and LPs with negation as failure in their head. We then extend this result to so-called set-stable ABA semantics, originally defined for the fragment of non-flat ABA called bipolar ABA. We showcase how to define set-stable semantics for LPs with negation as failure in their head and show the correspondence to set-stable ABA semantics.

Related papers

• Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation [18.32141673219938]
  We study an instantiation-based approach for reasoning in possibly non-flat ABA. We propose two algorithmic approaches for reasoning in possibly non-flat ABA.
  arXiv  Detail & Related papers  (2024-04-17T14:36:47Z)
• Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning [55.4510979153023]
  In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. To help these mislabeled samples "appeal," we propose the first appeal-based framework.
  arXiv  Detail & Related papers  (2023-12-18T09:09:52Z)
• Exploiting Uncertainty for Querying Inconsistent Description Logics Knowledge Bases [0.3683202928838613]
  We exploit an existing probabilistic semantics called DISPONTE to overcome this problem. We implement our approach in the reasoners TRILL and BUNDLE and empirically tested the validity of our proposal.
  arXiv  Detail & Related papers  (2023-06-15T13:50:46Z)
• Non-flat ABA is an Instance of Bipolar Argumentation [23.655909692988637]
  Assumption-based Argumentation (ABA) is a well-known structured argumentation formalism. A common restriction imposed on ABA frameworks (ABAFs) is that they are flat. No translation exists from general, possibly non-flat ABAFs into any kind of abstract argumentation formalism.
  arXiv  Detail & Related papers  (2023-05-21T13:18:08Z)
• Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures [104.32063681736349]
  We present an approach to describe predicate-argument structures using natural language definitions instead of discrete labels. Our experiments and analyses on PropBank-style and FrameNet-style, dependency-based and span-based SRL also demonstrate that a flexible model with an interpretable output does not necessarily come at the expense of performance.
  arXiv  Detail & Related papers  (2022-12-02T11:19:16Z)
• Admissibility in Strength-based Argumentation: Complexity and Algorithms (Extended Version with Proofs) [1.5828697880068698]
  We study the adaptation of admissibility-based semantics to Strength-based Argumentation Frameworks (StrAFs) Especially, we show that the strong admissibility defined in the literature does not satisfy a desirable property, namely Dung's fundamental lemma. We propose a translation in pseudo-Boolean constraints for computing (strong and weak) extensions.
  arXiv  Detail & Related papers  (2022-07-05T18:42:04Z)
• Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction [52.63803634033647]
  Low-resource relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce. We develop a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate the gradient descent direction on labeled data. We also propose a framework called GradLRE, which handles two major scenarios in low-resource relation extraction.
  arXiv  Detail & Related papers  (2021-09-14T03:51:15Z)
• Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction [54.06292969184476]
  We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Our approach employs semantic loss which captures the precise meaning of a logical sentence. With a focus on low-data regimes, we show that semantic loss outperforms the baselines by a wide margin.
  arXiv  Detail & Related papers  (2021-03-20T00:16:29Z)
• On Irrelevant Literals in Pseudo-Boolean Constraint Learning [21.506382989223784]
  We show that emphirrelevant literals may lead to infer constraints that are weaker than they should be. This suggests that current implementations of PB solvers based on cutting planes should be reconsidered to prevent the generation of irrelevant literals.
  arXiv  Detail & Related papers  (2020-12-08T13:52:09Z)
• On the Sentence Embeddings from Pre-trained Language Models [78.45172445684126]
  In this paper, we argue that the semantic information in the BERT embeddings is not fully exploited. We find that BERT always induces a non-smooth anisotropic semantic space of sentences, which harms its performance of semantic similarity. We propose to transform the anisotropic sentence embedding distribution to a smooth and isotropic Gaussian distribution through normalizing flows that are learned with an unsupervised objective.
  arXiv  Detail & Related papers  (2020-11-02T13:14:57Z)
• A Weaker Faithfulness Assumption based on Triple Interactions [89.59955143854556]
  We propose a weaker assumption that we call $2$-adjacency faithfulness. We propose a sound orientation rule for causal discovery that applies under weaker assumptions.
  arXiv  Detail & Related papers  (2020-10-27T13:04:08Z)

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.