On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI
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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
- 2018Abduction-Based Explanations for Machine Learning Modelscited by 159
- 2018A Symbolic Approach to Explaining Bayesian Network Classifierscited by 130
- 2020On The Reasons Behind Decisionscited by 99
- 2017Verifying Properties of Binarized Deep Neural Networkscited by 77
- 2020On Tractable Representations of Binary Neural Networkscited by 34
- 2019On Validating, Repairing and Refining Heuristic ML Explanationscited by 32
- 2020On Symbolically Encoding the Behavior of Random Forestscited by 15
- 2018On the Relative Succinctness of Sentential Decision Diagramscited by 6
- 2015New Limits for Knowledge Compilation and Applications to Exact Model Countingcited by 5
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Citing papers per year: 2022: 6; 2023: 6; 2024: 0; 2025: 1; 2026: 3.
16 papers.
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- 2022Logic-Based Explainability in Machine Learningcited by 47
- 2022On Tackling Explanation Redundancy in Decision Treescited by 44
- 2022On the Computation of Necessary and Sufficient Explanationscited by 23
- 2022Eliminating The Impossible, Whatever Remains Must Be Truecited by 23
- 2023Logic for Explainable AIcited by 23
- 2022Feature Necessity & Relevancy in ML Classifier Explanationscited by 22
- 2023Disproving XAI Myths with Formal Methods -- Initial Resultscited by 12
- 2023On Formal Feature Attribution and Its Approximationcited by 11
- 2025Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasonscited by 11
- 2022Tractable Boolean and Arithmetic Circuitscited by 9
- 2023A New Class of Explanations for Classifiers with Non-Binary Featurescited by 8
- 2023On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuitscited by 5
4 more
- 2026Circuit Representations of Random Forests with Applications to XAIcited by 2
- 2023On Logic-Based Explainability with Partially Specified Inputscited by 0
- 2026Scaling the Explanation of Multi-Class Bayesian Network Classifierscited by 0
- 2026Explaining Reinforcement Learning Agents via Inductive Logic Programmingcited by 0
- 2026Explaining Reinforcement Learning Agents via Inductive Logic Programming15 Jul 2026
- 2026Scaling the Explanation of Multi-Class Bayesian Network Classifiers15 Mar 2026
- 2026Circuit Representations of Random Forests with Applications to XAI9 Feb 2026
- 2025Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons5 Feb 2025
- 2023On Formal Feature Attribution and Its Approximation7 Jul 2023
- 2023On Logic-Based Explainability with Partially Specified Inputs27 Jun 2023
- 2023Disproving XAI Myths with Formal Methods -- Initial Results13 May 2023
- 2023Logic for Explainable AI9 May 2023
- 2023A New Class of Explanations for Classifiers with Non-Binary Features28 Apr 2023
- 2023On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuits30 Jan 2023
- 2022Feature Necessity & Relevancy in ML Classifier Explanations27 Oct 2022
- 2022Logic-Based Explainability in Machine Learning24 Oct 2022
4 more
- 2022Eliminating The Impossible, Whatever Remains Must Be True20 Jun 2022
- 2022On Tackling Explanation Redundancy in Decision Trees20 May 2022
- 2022On the Computation of Necessary and Sufficient Explanations20 Mar 2022
- 2022Tractable Boolean and Arithmetic Circuits7 Feb 2022
- 2022Logic-Based Explainability in Machine Learningcited by 47
- 2022On Tackling Explanation Redundancy in Decision Treescited by 44
- 2022On the Computation of Necessary and Sufficient Explanationscited by 23
- 2022Eliminating The Impossible, Whatever Remains Must Be Truecited by 23
- 2023Logic for Explainable AIcited by 23
- 2022Feature Necessity & Relevancy in ML Classifier Explanationscited by 22
- 2023Disproving XAI Myths with Formal Methods -- Initial Resultscited by 12
- 2023On Formal Feature Attribution and Its Approximationcited by 11
- 2025Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasonscited by 11
- 2022Tractable Boolean and Arithmetic Circuitscited by 9
- 2023A New Class of Explanations for Classifiers with Non-Binary Featurescited by 8
- 2023On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuitscited by 5
12 most cited of 16 All 16 (JSON)
- 2026Explaining Reinforcement Learning Agents via Inductive Logic Programming15 Jul 2026
- 2026Scaling the Explanation of Multi-Class Bayesian Network Classifiers15 Mar 2026
- 2026Circuit Representations of Random Forests with Applications to XAI9 Feb 2026
- 2025Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons5 Feb 2025
- 2023On Formal Feature Attribution and Its Approximation7 Jul 2023
- 2023On Logic-Based Explainability with Partially Specified Inputs27 Jun 2023
- 2023Disproving XAI Myths with Formal Methods -- Initial Results13 May 2023
- 2023Logic for Explainable AI9 May 2023
- 2023A New Class of Explanations for Classifiers with Non-Binary Features28 Apr 2023
- 2023On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuits30 Jan 2023
- 2022Feature Necessity & Relevancy in ML Classifier Explanations27 Oct 2022
- 2022Logic-Based Explainability in Machine Learning24 Oct 2022
12 newest of 16 All 16 (JSON)