# Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)

arXiv: https://arxiv.org/abs/2107.06413 · HTML: https://mlsanity.com/paper/2107.06413/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 60 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Properties of ABA+ for Non-Monotonic Reasoning](https://mlsanity.com/paper/1603.08714/) (2016)
- [Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation](https://mlsanity.com/paper/2007.05284/) (2020)

## Built on by (most cited first, 6 of 6)

- [Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)](https://mlsanity.com/paper/2408.00108/) (2024)
- [On Interactive Explanations as Non-Monotonic Reasoning](https://mlsanity.com/paper/2208.00316/) (2022)
- [Neuro-Argumentative Learning with Case-Based Reasoning](https://mlsanity.com/paper/2505.15742/) (2025)
- [Supported Abstract Argumentation for Case-Based Reasoning](https://mlsanity.com/paper/2507.04994/) (2025)
- [Object-Centric Case-Based Reasoning via Argumentation](https://mlsanity.com/paper/2510.00185/) (2025)
- [Technical Report on the Learning of Case Relevance in Case-Based Reasoning with Abstract Argumentation](https://mlsanity.com/paper/2310.19607/) (2023)

## Abstract (arXiv)

Recently, abstract argumentation-based models of case-based reasoning (AAtext - CBR in short) have been proposed, originally inspired by the legal domain, but also applicable as classifiers in different scenarios. However, the formal properties of AAtext - CBR as a reasoning system remain largely unexplored. In this paper, we focus on analysing the non-monotonicity properties of a regular version of AAtext - CBR (that we call AAtext - CBRsucceq). Specifically, we prove that AAtext - CBRsucceq is not cautiously monotonic, a property frequently considered desirable in the literature. We then define a variation of AAtext - CBRsucceq which is cautiously monotonic. Further, we prove that such variation is equivalent to using AAtext - CBRsucceq with a restricted casebase consisting of all "surprising" and "sufficient" cases in the original casebase. As a by-product, we prove that this variation of AAtext - CBRsucceq is cumulative, rationally monotonic, and empowers a principled treatment of noise in "incoherent" casebases. Finally, we illustrate AAtext - CBR and cautious monotonicity questions on a case study on the U.S. Trade Secrets domain, a legal casebase.