The problem
Traditional ABA semantics classify assumptions through extensions or labels. Many applied settings need a finer account of how strongly competing assumptions are supported.
The approach
The paper abstracts potentially non-flat ABA frameworks into bipolar set-based argumentation frameworks and generalises modular gradual semantics to assign dialectical strengths directly to assumptions.
What the work establishes
- Introduces a family of gradual semantics specifically for assumption-based argumentation.
- Shows adaptations of desirable balance and monotonicity properties.
- Compares the proposal with an argument-based baseline and assesses convergence on synthetic ABA frameworks.
Why it matters to CArLA
Gradual evaluation gives CArLA a route beyond yes-or-no decisions, supporting explanations that expose relative support and uncertainty when evidence is noisy or incomplete.
Citation
Rapberger, A., Russo, F., Rago, A., & Toni, F. (2025). On Gradual Semantics for Assumption-Based Argumentation. Proceedings of KR 2025, 512–522. https://doi.org/10.24963/kr.2025/50