Learning General Constraints in CSP

Veksler, M. A.; Strichman, Ofer

doi:10.1007/978-3-319-18008-3_28

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…In the case of parallel portfolio solvers, a subset of solvers is run in parallel until a solution is found or, if the optimal solution is wanted, it can exchange information about intermediate solutions found during the search, e.g., sharing the best found objective bound [49]. Popular portfolio solvers include SATZilla [28], CPHydra [29], Sunny-CP [50], or HaifaCSP [51].…”

Section: Algorithm Selection and Configurationmentioning

confidence: 99%

Predictive Machine Learning of Objective Boundaries for Solving COPs

Spieker

Gotlieb

2021

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Solving Constraint Optimization Problems (COPs) can be dramatically simplified by boundary estimation, that is providing tight boundaries of cost functions. By feeding a supervised Machine Learning (ML) model with data composed of the known boundaries and extracted features of COPs, it is possible to train the model to estimate the boundaries of a new COP instance. In this paper, we first give an overview of the existing body of knowledge on ML for Constraint Programming (CP), which learns from problem instances. Second, we introduce a boundary estimation framework that is applied as a tool to support a CP solver. Within this framework, different ML models are discussed and evaluated regarding their suitability for boundary estimation, and countermeasures to avoid unfeasible estimations that avoid the solver finding an optimal solution are shown. Third, we present an experimental study with distinct CP solvers on seven COPs. Our results show that near-optimal boundaries can be learned for these COPs with only little overhead. These estimated boundaries reduce the objective domain size by 60-88% and can help the solver find near-optimal solutions early during the search.

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Section: Algorithm Selection and Configurationmentioning

confidence: 99%

Predictive Machine Learning of Objective Boundaries for Solving COPs

Spieker

Gotlieb

2021

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“…As aforementioned, a great deal of research on signed logic has been conducted in satisfiability solving, logic programming and constraint solving, but signed logic and its variants have also been handled, during the last twenty years, in many aproximate reasoning scenarios such as model-based diagnosis [49], signed optimization [8], signed randomiation [17], combining signed logic and linear integer arithmetic [7], learning in CSP [98], comparing resolution proofs and CDCL-with-restarts [82], regular belief merging [36], in the formalization of a recent real-world multivalued-logic [47] and more.…”

Section: General Presentationmentioning

confidence: 99%

A First Polynomial Non-Clausal Class in Many-Valued Logic

Imaz¹

2021

Preprint

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The relevance of polynomial formula classes to deductive efficiency motivated their search, and currently, a great number of such classes is known. Nonetheless, they have been exclusively sought in the setting of clausal form and propositional logic, which is of course expressively limiting for real applications. As a consequence, a first polynomial propositional class in non-clausal (NC) form has recently been proposed.Along these lines and towards making NC tractability applicable beyond propositional logic, firstly, we define the Regular many-valued Horn Non-Clausal class, or RH, obtained by suitably amalgamating both regular classes: Horn and NC.Secondly, we demonstrate that the relationship between (1) RH and the regular Horn class is that syntactically RH subsumes the Horn class but that both classes are equivalent semantically; and between (2) RH and the regular non-clausal class is that RH contains all NC formulas whose clausal form is Horn.Thirdly, we define Regular Non-Clausal Unit-Resolution, or RUR NC , and prove both that it is complete for RH and that checks its satisfiability in polynomial time. The latter fact shows that our intended goal is reached since RH is many-valued, non-clausal and tractable.As RH and RUR NC are, both, basic in the DPLL scheme, the most efficient in propositional logic, and can be extended to some other non-classical logics, we argue that they pave the way for efficient non-clausal DPLL-based approximate reasoning.Field: Tractable Approximate Automated Reasoning.

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“…This method extends naturally to word-level learning for bitvectors, yet our own approach is more general, as our learnt constraints do not mention particular assignments, they are solely derived from the relationship between the propagation clauses of the constraints that lead to a conflict. The HaifaCSP solver [28] is able to learn general constraints through a resolution-inspired method in a more general and concise way than nogood assignments. The approach uses a non-clausal learning that relies on direct inference between the constraints themselves as well as SAT inspired techniques, notably a similar heuristic to VSIDS [3] for variable ordering.…”

Section: Learning In Constraintmentioning

confidence: 99%

CDCL-inspired Word-level Learning for Bit-vector Constraint Solving

Chihani,

Bobot,

Bardin

2017

Preprint

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The theory of quantifier-free bitvectors is of paramount importance in software verification. The standard approach for satisfiability checking reduces the bitvector problem to a Boolean problem, leveraging the powerful SAT solving techniques and their conflict-driven clause learning (CDCL) mechanisms. Yet, this bit-level approach loses the structure of the initial bitvector problem. We propose a conflictdriven, word-level, combinable constraints learning for the theory of quantifier-free bitvectors. This work paves the way to truly word-level decision procedures for bitvectors, taking full advantage of word-level propagations recently designed in CP and SMT communities.

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Learning General Constraints in CSP

Cited by 9 publications

References 14 publications

Predictive Machine Learning of Objective Boundaries for Solving COPs

Predictive Machine Learning of Objective Boundaries for Solving COPs

A First Polynomial Non-Clausal Class in Many-Valued Logic

CDCL-inspired Word-level Learning for Bit-vector Constraint Solving

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