MaxSAT Evaluation 2018: New Developments and Detailed Results

Bacchus, Fahiem; Järvisalo, Matti; Martins, Ruben

doi:10.3233/sat190119

Cited by 20 publications

(33 citation statements)

References 24 publications

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“…We begin by detailing the instantiations of Lin-Search and Core-Guided that we use in the prototype core-boosted linear search algorithm experimented with in the next section. As the linear search component we use the basic Lin-Search extended with varying resolution and solution-based phase-saving in the style of LinSBPS, the best performing solver of the incomplete 300s track of the 2018 MaxSAT evaluation [14]. Solution-based phase-saving is a heuristic designed to steer the search towards the currently best known solution by modifying the branching heuristic of the internal SAT solver to always prefer setting the polarity of a literal it branches on to equal its polarity in the currently best known solution.…”

Section: Related Workmentioning

confidence: 99%

“…The solver SATLike [30] deviates from this description and instead uses local-search techniques in order to quickly traverse the search space and look for solutions of increasing quality. A more detailed description of the solvers can be found on the evaluation homepage [14].…”

Section: Related Workmentioning

confidence: 99%

“…To demonstrate the potential of core-boosted linear search we report on an experimental evaluation of a prototype solver that combines reimplementations of two state-of-the-art MaxSAT solvers, PMRES [44] as the core-guided algorithm and LinSBPS [14] as the linear algorithm. We compare core-boosted linear search to its individual components on a standard set of benchmarks.…”

Section: Introductionmentioning

confidence: 99%

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Core-Guided and Core-Boosted Search for CP

Gange

Berg

Demirović

et al. 2020

Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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Maximum Satisfiability (MaxSAT), the optimisation extension of the well-known Boolean Satisfiability (SAT) problem, is a competitive approach for solving NP-hard problems encountered in various artificial intelligence and industrial domains. Due to its computational complexity, there is an inherent tradeoff between scalability and guarantee on solution quality in MaxSAT solving. Limitations on available computational resources in many practical applications motivate the development of complete any-time MaxSAT solvers, i.e. algorithms that compute optimal solutions while providing intermediate results. In this work, we propose core-boosted linear search, a generic search-strategy that combines two central approaches in modern MaxSAT solving, namely linear and core-guided algorithms. Our experimental evaluation on a prototype combining reimplementations of two state-of-the-art MaxSAT solvers, PMRES as the core-guided approach and LinSBPS as the linear algorithm, demonstrates that our core-boosted linear algorithm often outperforms its individual components and shows competitive and, on many domains, superior results when compared to other state-of-the-art solvers for incomplete MaxSAT solving.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Core-Guided and Core-Boosted Search for CP

Gange

Berg

Demirović

et al. 2020

Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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show abstract

“…• Instance Parser: The component Instance Parser parses the input specifications and generates an internal representation for the query and the accompanying RBAC model. • MaxSAT Encoder: Given the internal representation, the component MaxSAT Encoder generates the corresponding MaxSAT formulae in WCNF format [5]. Among others, these formulae include clauses for RBAC relations (UA, PA), optimization objective and DMER constraints.…”

Section: Architecturementioning

confidence: 99%

“…The current version of AQUA promptly supports a range of MaxSAT solvers including: qmaxsat [9], Sat4j [7], MaxSATz [8], WPM1 [2], maxino [1] and Loandra [6]. New MaxSAT solvers can be easily integrated thanks to the standard format specification provided by the MaxSAT competition [5] in input and output formats.…”

Section: A Maxsat Integrationmentioning

confidence: 99%

Aqua

Armando

Gazzarata

Türkmen

2020

Proceedings of the 25th ACM Symposium on Access Control Models and Technologies

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We present AQUA, a solver for the User Authorization Query (UAQ) problem in Role-Based Access Control (RBAC). The UAQ problem amounts to determining a set of roles granting a given set of permissions, satisfying a collection of authorisation constraints (most notably Dynamic Mutually-Exclusive Roles, DMER) and achieving some optimization objective, i.e. seeking min/max/any number of roles to activate and/or permissions to grant. AQUA supports the enforcement of a wide class of DMER constraints as well as several types of optimization objectives (namely, min/max/any number of roles to activate, min/max/any number of permissions to grant, and a combinations thereof). In this paper, we demonstrate the use of AQUA over a running example while providing certain implementation details including the architecture.

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Abstract Cores in Implicit Hitting Set MaxSat Solving

Berg

Bacchus

Poole

2020

Lecture Notes in Computer Science

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Maximum Satisfiability (MaxSat) solving is an active area of research motivated by numerous successful applications to solving NPhard combinatorial optimization problems. One of the most successful approaches to solving MaxSat instances arising from real world applications is the Implicit Hitting Set (IHS) approach. IHS solvers are complete MaxSat solvers that harness the strengths of both Boolean Satisfiability (SAT) and Integer Linear Programming (IP) solvers by decoupling core-extraction and optimization. While such solvers show state-of-theart performance on many instances, it is known that there exist MaxSat instances on which IHS solvers need to extract an exponential number of cores before terminating. Motivated by the structure of the simplest of these problematic instances, we propose a technique we call abstract cores that provides a compact representation for a potentially exponential number of regular cores. We demonstrate how to incorporate abstract core reasoning into the IHS algorithm and report on an empirical evaluation demonstrating that including abstract cores into a state-of-the-art IHS solver improves its performance enough to surpass the best performing solvers of the most recent 2019 MaxSat Evaluation.

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MaxSAT Evaluation 2018: New Developments and Detailed Results

Cited by 20 publications

References 24 publications

Core-Guided and Core-Boosted Search for CP

Core-Guided and Core-Boosted Search for CP

Aqua

Abstract Cores in Implicit Hitting Set MaxSat Solving

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