mlsanityData to 7 Oct 2026

On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI

arXiv 2108.09876 ↗First posted 23 Aug 2021cs.AI cs.DB cs.LGcited by 16

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Abstract (arXiv)

Quantified Boolean logic results from adding operators to Boolean logic for existentially and universally quantifying variables. This extends the reach of Boolean logic by enabling a variety of applications that have been explored over the decades. The existential quantification of literals (variable states) and its applications have also been studied in the literature. In this paper, we complement this by studying universal literal quantification and its applications, particularly to explainable AI. We also provide a novel semantics for quantification, discuss the interplay between variable/literal and existential/universal quantification. We further identify some classes of Boolean formulas and circuits on which quantification can be done efficiently. Literal quantification is more fine-grained than variable quantification as the latter can be defined in terms of the former. This leads to a refinement of quantified Boolean logic with literal quantification as its primitive.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 9 of its 79 references that are in the map. First posted on arXiv 23 Aug 2021.

Builds on

9 of 79 references.

References · 9

  1. 2018Abduction-Based Explanations for Machine Learning Modelscited by 159
  2. 2018A Symbolic Approach to Explaining Bayesian Network Classifierscited by 130
  3. 2020On The Reasons Behind Decisionscited by 99
  4. 2017Verifying Properties of Binarized Deep Neural Networkscited by 77
  5. 2020On Tractable Representations of Binary Neural Networkscited by 34
  6. 2019On Validating, Repairing and Refining Heuristic ML Explanationscited by 32
  7. 2020On Symbolically Encoding the Behavior of Random Forestscited by 15
  8. 2018On the Relative Succinctness of Sentential Decision Diagramscited by 6
  9. 2015New Limits for Knowledge Compilation and Applications to Exact Model Countingcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 6; 2024: 0; 2025: 1; 2026: 3.

16 papers.

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