# Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)

arXiv: https://arxiv.org/abs/2408.00108 · HTML: https://mlsanity.com/paper/2408.00108/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [A General Account of Argumentation with Preferences](https://mlsanity.com/paper/1804.06763/) (2018)
- [ABA+: Assumption-Based Argumentation with Preferences](https://mlsanity.com/paper/1610.03024/) (2016)
- [On the Acceptability of Arguments in Preference-Based Argumentation](https://mlsanity.com/paper/1301.7358/) (2013)
- [Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract Argumentation (with Appendix)](https://mlsanity.com/paper/2107.06413/) (2021)

## Built on by (most cited first, 4 of 4)

- [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)
- [Deep Arguing](https://mlsanity.com/paper/2605.10569/) (2026)

## Abstract (arXiv)

In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show that previous abstract argumentation for case-based reasoning approaches are insufficient at expressing preferences over constituents of an argument. We then demonstrate how this can be applied to a real-world medical dataset sourced from a clinical trial evaluating differing assessment methods of patients with a primary brain tumour. We show empirically that our approach outperforms other interpretable machine learning models on this dataset.