Eliciting Rational Initial Weights in Gradual Argumentation

• URL: http://arxiv.org/abs/2502.07452v1
• Date: Tue, 11 Feb 2025 10:52:54 GMT
• Title: Eliciting Rational Initial Weights in Gradual Argumentation
• Authors: Nir Oren, Bruno Yun,
• Abstract summary: We propose an elicitation pipeline that allows one to specify acceptability degree intervals for each argument. By employing gradual semantics, we can refine these intervals when they are rational, restore rationality when they are not, and ultimately identify possible initial weights for each argument.
• Score: 3.660182910533372
• License:
• Abstract: Many semantics for weighted argumentation frameworks assume that each argument is associated with an initial weight. However, eliciting these initial weights poses challenges: (1) accurately providing a specific numerical value is often difficult, and (2) individuals frequently confuse initial weights with acceptability degrees in the presence of other arguments. To address these issues, we propose an elicitation pipeline that allows one to specify acceptability degree intervals for each argument. By employing gradual semantics, we can refine these intervals when they are rational, restore rationality when they are not, and ultimately identify possible initial weights for each argument.

Related papers

• Abstract Weighted Based Gradual Semantics in Argumentation Theory [3.2566808526538873]
  We introduce four important problems linking gradual semantics and acceptability degrees. First, we reexamine the inverse problem, seeking to identify the argument weights of the argumentation framework which lead to a specific final acceptability degree. Third, we ask whether argument weights can be found when preferences, rather than acceptability degrees for arguments are considered. Fourth, we consider the topology of the space of valid acceptability degrees, asking whether "gaps" exist in this space.
  arXiv  Detail & Related papers  (2024-01-21T12:22:48Z)
• LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning [61.7853049843921]
  Chain-of-thought (CoT) prompting is a popular in-context learning approach for large language models (LLMs) This paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales.
  arXiv  Detail & Related papers  (2023-12-07T20:36:10Z)
• 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)
• A Hybrid System for Systematic Generalization in Simple Arithmetic Problems [70.91780996370326]
  We propose a hybrid system capable of solving arithmetic problems that require compositional and systematic reasoning over sequences of symbols. We show that the proposed system can accurately solve nested arithmetical expressions even when trained only on a subset including the simplest cases.
  arXiv  Detail & Related papers  (2023-06-29T18:35:41Z)
• Enriching Disentanglement: From Logical Definitions to Quantitative Metrics [59.12308034729482]
  Disentangling the explanatory factors in complex data is a promising approach for data-efficient representation learning. We establish relationships between logical definitions and quantitative metrics to derive theoretically grounded disentanglement metrics. We empirically demonstrate the effectiveness of the proposed metrics by isolating different aspects of disentangled representations.
  arXiv  Detail & Related papers  (2023-05-19T08:22:23Z)
• Inferring Attack Relations for Gradual Semantics [5.254093731341154]
  A gradual semantics takes a weighted argumentation framework as input and outputs a final acceptability degree for each argument. We seek to determine whether there is a set of attacks on those arguments such that we can obtain these acceptability degrees.
  arXiv  Detail & Related papers  (2022-11-29T11:45:27Z)
• Rationale-Augmented Ensembles in Language Models [53.45015291520658]
  We reconsider rationale-augmented prompting for few-shot in-context learning. We identify rationale sampling in the output space as the key component to robustly improve performance. We demonstrate that rationale-augmented ensembles achieve more accurate and interpretable results than existing prompting approaches.
  arXiv  Detail & Related papers  (2022-07-02T06:20:57Z)
• Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations [71.2950434944196]
  We develop Maieutic Prompting, which infers a correct answer to a question even from the noisy and inconsistent generations of language models. Maieutic Prompting achieves up to 20% better accuracy than state-of-the-art prompting methods.
  arXiv  Detail & Related papers  (2022-05-24T06:36:42Z)
• Analytical Solutions for the Inverse Problem within Gradual Semantics [3.957174470017176]
  We show how an analytical approach can be used to solve the inverse problem in gradual semantics. Unlike the current state-of-the-art, such an approach can rapidly find a solution, and is guaranteed to do so.
  arXiv  Detail & Related papers  (2022-03-02T15:55:10Z)
• The Inverse Problem for Argumentation Gradual Semantics [8.860629791560198]
  A sub-class of such semantics, the so-called weighted semantics, takes an initial set of weights over the arguments as input. We consider the inverse problem over such weighted semantics. That is, given an argumentation framework and a desired argument ranking, we ask whether there exist initial weights such that a particular semantics produces the given ranking.
  arXiv  Detail & Related papers  (2022-02-01T09:46:23Z)
• Intrinsic Argument Strength in Structured Argumentation: a Principled Approach [0.0]
  We study methods for assigning an argument its intrinsic strength, based on the strengths of the premises and inference rules used to form said argument. We first define a set of principles, which are properties that strength assigning methods might satisfy. We then propose two such methods and analyse which principles they satisfy.
  arXiv  Detail & Related papers  (2021-09-01T11:54:15Z)

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.