Rejecting Arguments Based on Doubt in Structured Bipolar Argumentation

2026-02-03

AI Summary

This work addresses a key limitation in traditional computational argumentation models, which mandate the acceptance of all admissible arguments and operate at the level of whole arguments, thereby failing to capture the natural human tendency to skeptically reject claims and reason at the granularity of individual sentences. To overcome this, the paper proposes a Structured Bipolar Argumentation Framework (SBAF) that incorporates both attack and support relations, along with a novel para-complete semantics that permits rational rejection of otherwise admissible arguments. Crucially, the framework outputs extensions as sets of sentences rather than sets of arguments, aligning more closely with real-world reasoning practices. The proposed semantics lies strictly between admissible and complete semantics, generalizes deductive support semantics as a special case, and clarifies the applicability boundaries of abstract argumentation, offering a more flexible and cognitively plausible modeling paradigm.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper develops a new approach to computational argumentation that is informed by philosophical and linguistic views. Namely, it takes into account two ideas that have received little attention in the literature on computational argumentation: First, an agent may rationally reject an argument based on mere doubt, thus not all arguments they could defend must be accepted; and, second, that it is sometimes more natural to think in terms of which individual sentences or claims an agent accepts in a debate, rather than which arguments. In order to incorporate these two ideas into a computational approach, we first define the notion of structured bipolar argumentation frameworks (SBAFs), where arguments consist of sentences and we have both an attack and a support relation between them. Then, we provide semantics for SBAFs with two features: (1) Unlike with completeness-based semantics, our semantics do not force agents to accept all defended arguments. (2) In addition to argument extensions, which give acceptable sets of arguments, we also provide semantics for language extensions that specify acceptable sets of sentences. These semantics represent reasonable positions an agent might have in a debate. Our semantics lie between the admissible and complete semantics of abstract argumentation. Further, our approach can be used to provide a new perspective on existing approaches. For instance, we can specify the conditions under which an agent can ignore support between arguments (i.e. under which the use of abstract argumentation is warranted) and we show that deductive support semantics is a special case of our approach.

Problem

• structured bipolar argumentation
• doubt-based rejection
• sentence-level acceptance
• argumentation semantics
• computational argumentation

Innovation

• structured bipolar argumentation
• doubt-based rejection
• language extensions
• non-complete semantics
• sentence-level acceptance