# Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation

arXiv: https://arxiv.org/abs/2007.05284 · HTML: https://mlsanity.com/paper/2007.05284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)](https://mlsanity.com/paper/2107.06413/) (2021)

## 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, including image classification, sentiment analysis of text, and in predicting the passage of bills in the UK Parliament. 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 of non-monotonic reasoning. We then define a variation of AAtext -CBRsucceq which is cautiously monotonic, and provide an algorithm for obtaining it. Further, we prove that such variation is equivalent to using AAtext -CBRsucceq with a restricted casebase consisting of all "surprising" cases in the original casebase.