mlsanityData to 7 Oct 2026

Explainability via Short Formulas: the Case of Propositional Logic with Implementation

arXiv 2209.01403 ↗First posted 3 Sep 2022cs.AI cs.LOcited by 4

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Abstract (arXiv)

We conceptualize explainability in terms of logic and formula size, giving a number of related definitions of explainability in a very general setting. Our main interest is the so-called special explanation problem which aims to explain the truth value of an input formula in an input model. The explanation is a formula of minimal size that (1) agrees with the input formula on the input model and (2) transmits the involved truth value to the input formula globally, i.e., on every model. As an important example case, we study propositional logic in this setting and show that the special explainability problem is complete for the second level of the polynomial hierarchy. We also provide an implementation of this problem in answer set programming and investigate its capacity in relation to explaining answers to the n-queens and dominating set problems.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 2 of its 15 references that are in the map. First posted on arXiv 3 Sep 2022.

Builds on

2 of 15 references.

References · 2

  1. 2018A Symbolic Approach to Explaining Bayesian Network Classifierscited by 130
  2. 2016Stable-Unstable Semantics: Beyond NP with Normal Logic Programscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.