# A Model-Agnostic SAT-based Approach for Symbolic Explanation Enumeration

Source: [A Model-Agnostic SAT-based Approach for Symbolic Explanation Enumeration](<https://www.sota2.com/research/paper/68fa6d3c1e209131cea38ab7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:29:35.891Z

Authors: Ryma Boumazouza, Fahima Cheikh-Alili, Bertrand Mazure, Karim Tabia

Published: 2022-06-23T08:35:47.000Z

## Abstract

In this paper titled A Model-Agnostic SAT-based approach for Symbolic Explanation Enumeration we propose a generic agnostic approach allowing to generate different and complementary types of symbolic explanations. More precisely, we generate explanations to locally explain a single prediction by analyzing the relationship between the features and the output. Our approach uses a propositional encoding of the predictive model and a SAT-based setting to generate two types of symbolic explanations which are Sufficient Reasons and Counterfactuals. The experimental results on image classification task show the feasibility of the proposed approach and its effectiveness in providing Sufficient Reasons and Counterfactuals explanations.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.11539>)

[PDF](<https://arxiv.org/pdf/2206.11539.pdf>)