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Efficient conflict driven learning in a boolean satisfiability solver

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TLDR
This paper generalizes various conflict driven learning strategies in terms of different partitioning schemes of the implication graph to re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes.
Abstract
One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.

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Journal ArticleDOI

Directed test generation for validation of multicore architectures

TL;DR: This paper proposes a novel technique that exploits the structural similarity within the same bound as well as between different bounds to enable the reuse of the knowledge learned from one core to the remaining cores in multicore architectures.

An Effective QBF Solver for Planning Problems.

TL;DR: A first implementation of the solution algorithm is tested, based on backtracking search and on a new form of learning clauses, that is significantly faster than current state-of-the-art QBF solvers.
Proceedings ArticleDOI

Test generation for non-separable RTL controller-datapath circuits using a satisfiability based approach

TL;DR: A satisfiability-based algorithm for automatically generating test sequences that target gate-level stuck-at faults in a circuit by using its register-transfer level (RTL) description, which reduces RTL test generation to a satisfiability (SAT) instance that has a significantly lower complexity than the equivalent problem at the gate- level.
Proceedings ArticleDOI

PaMira - A Parallel SAT Solver with Knowledge Sharing

TL;DR: PaMira is a powerful distributed SAT solver based on the highly optimized, sequential SAT engine Mira, incorporating all essential optimization techniques modern algorithms utilize to maximize performance.
Proceedings Article

Learning abductive reasoning using random examples

TL;DR: A new formulation of abduction is considered in which degrees of "plausibility" of explanations, along with the rules of the domain, are learned from concrete examples (settings of attributes) and falls in the "learning to reason" framework of Khardon and Roth.
References
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Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book

Introduction to Algorithms

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book

Genetic Algorithms

Journal ArticleDOI

Tabu Search—Part II

TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.