On enumerating short projected models.

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Title: On enumerating short projected models.
Authors: Möhle, Sibylle1 (AUTHOR) smoehle@acm.org, Sebastiani, Roberto2 (AUTHOR), Biere, Armin3 (AUTHOR)
Source: Discrete Applied Mathematics. Jan2025, Vol. 361, p412-439. 28p.
Subjects: Integrated circuit verification, Systems engineering, Proposition (Logic), Calculus, Algorithms
Abstract: Propositional model enumeration, or All-SAT, is the task to record all models of a propositional formula. It is a key task in software and hardware verification, system engineering, and predicate abstraction, to mention a few. It also provides a means to convert a CNF formula into DNF, which is relevant in circuit design. While in some applications enumerating models multiple times causes no harm, in others avoiding repetitions is crucial. We therefore present two model enumeration algorithms which adopt dual reasoning in order to shorten the found models. The first method enumerates pairwise contradicting models. Repetitions are avoided by the use of so-called blocking clauses for which we provide a dual encoding. In our second approach we relax the uniqueness constraint. We present an adaptation of the standard conflict-driven clause learning procedure to support model enumeration without blocking clauses. Our procedures are expressed by means of a calculus and proofs of correctness are provided. [ABSTRACT FROM AUTHOR]
Copyright of Discrete Applied Mathematics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Propositional model enumeration, or All-SAT, is the task to record all models of a propositional formula. It is a key task in software and hardware verification, system engineering, and predicate abstraction, to mention a few. It also provides a means to convert a CNF formula into DNF, which is relevant in circuit design. While in some applications enumerating models multiple times causes no harm, in others avoiding repetitions is crucial. We therefore present two model enumeration algorithms which adopt dual reasoning in order to shorten the found models. The first method enumerates pairwise contradicting models. Repetitions are avoided by the use of so-called blocking clauses for which we provide a dual encoding. In our second approach we relax the uniqueness constraint. We present an adaptation of the standard conflict-driven clause learning procedure to support model enumeration without blocking clauses. Our procedures are expressed by means of a calculus and proofs of correctness are provided. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Discrete Applied Mathematics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.dam.2024.10.021
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      – Code: eng
        Text: English
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        PageCount: 28
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      – SubjectFull: Integrated circuit verification
        Type: general
      – SubjectFull: Systems engineering
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      – SubjectFull: Proposition (Logic)
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      – SubjectFull: Calculus
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      – SubjectFull: Algorithms
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      – TitleFull: On enumerating short projected models.
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              Text: Jan2025
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              Y: 2025
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