GSAT with adaptive score function

GSAT is a well-known satisfiability search algorithm. In this paper we consider a modification of GSAT. In particular, we consider an adaptive score function. © 2013 Lhachmi El Badri et al

A system of intelligent algorithms for a module of onboard equipment of mobile vehicles

The area of intelligent robotics is moving from the single robot control problem to that of controlling multiple robots operating together and even collaborating in dynamic and unstructured intelligent environments. In such conditions, an intelligent robot control system is only part of general intelligent system. In this paper, we consider a model of such system. © 2013 Anna Gorbenko

Aplicação de formulas não-clausais em planejamento com redes de Petri /

Orientador : Fabiano SilvaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 2006Inclui bibliografi

Integration of constraint programming and linear programming techniques for constraint satisfaction problem and general constrained optimization problem.

Wong Siu Ham.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 131-138).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgments --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation for Integration --- p.2Chapter 1.2 --- Thesis Overview --- p.4Chapter 2 --- Preliminaries --- p.5Chapter 2.1 --- Constraint Programming --- p.5Chapter 2.1.1 --- Constraint Satisfaction Problems (CSP's) --- p.6Chapter 2.1.2 --- Satisfiability (SAT) Problems --- p.10Chapter 2.1.3 --- Systematic Search --- p.11Chapter 2.1.4 --- Local Search --- p.13Chapter 2.2 --- Linear Programming --- p.17Chapter 2.2.1 --- Linear Programming Problems --- p.17Chapter 2.2.2 --- Simplex Method --- p.19Chapter 2.2.3 --- Mixed Integer Programming Problems --- p.27Chapter 3 --- Integration of Constraint Programming and Linear Program- ming --- p.29Chapter 3.1 --- Problem Definition --- p.29Chapter 3.2 --- Related works --- p.30Chapter 3.2.1 --- Illustrating the Performances --- p.30Chapter 3.2.2 --- Improving the Searching --- p.33Chapter 3.2.3 --- Improving the representation --- p.36Chapter 4 --- A Scheme of Integration for Solving Constraint Satisfaction Prob- lem --- p.37Chapter 4.1 --- Integrated Algorithm --- p.38Chapter 4.1.1 --- Overview of the Integrated Solver --- p.38Chapter 4.1.2 --- The LP Engine --- p.44Chapter 4.1.3 --- The CP Solver --- p.45Chapter 4.1.4 --- Proof of Soundness and Completeness --- p.46Chapter 4.1.5 --- Compared with Previous Work --- p.46Chapter 4.2 --- Benchmarking Results --- p.48Chapter 4.2.1 --- Comparison with CLP solvers --- p.48Chapter 4.2.2 --- Magic Squares --- p.51Chapter 4.2.3 --- Random CSP's --- p.52Chapter 5 --- A Scheme of Integration for Solving General Constrained Opti- mization Problem --- p.68Chapter 5.1 --- Integrated Optimization Algorithm --- p.69Chapter 5.1.1 --- Overview of the Integrated Optimizer --- p.69Chapter 5.1.2 --- The CP Solver --- p.74Chapter 5.1.3 --- The LP Engine --- p.75Chapter 5.1.4 --- Proof of the Optimization --- p.77Chapter 5.2 --- Benchmarking Results --- p.77Chapter 5.2.1 --- Weighted Magic Square --- p.77Chapter 5.2.2 --- Template design problem --- p.78Chapter 5.2.3 --- Random GCOP's --- p.79Chapter 6 --- Conclusions and Future Work --- p.97Chapter 6.1 --- Conclusions --- p.97Chapter 6.2 --- Future work --- p.98Chapter 6.2.1 --- Detection of implicit equalities --- p.98Chapter 6.2.2 --- Dynamical variable selection --- p.99Chapter 6.2.3 --- Analysis on help of linear constraints --- p.99Chapter 6.2.4 --- Local Search and Linear Programming --- p.99Appendix --- p.101Proof of Soundness and Completeness --- p.101Proof of the optimization --- p.126Bibliography --- p.13

E-GENET: a stochastic constraint solver.

by Won, Hon Wing Stephen.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 95-101).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Constraint Satisfaction Problem --- p.1Chapter 1.2 --- CSP Solving Techniques --- p.2Chapter 1.3 --- Motivation of the Dissertation --- p.4Chapter 1.4 --- Overview of the Dissertation --- p.6Chapter 2 --- Related Work --- p.8Chapter 2.1 --- Heuristic Repair Method --- p.8Chapter 2.2 --- GSAT --- p.8Chapter 2.3 --- GENET --- p.9Chapter 2.4 --- Simulated Annealing --- p.9Chapter 2.5 --- Genetic Algorithms --- p.10Chapter 3 --- Overview of GENET --- p.11Chapter 3.1 --- Network Architecture --- p.11Chapter 3.2 --- Convergence Procedure --- p.12Chapter 3.3 --- The illegal and atmost Constraints --- p.13Chapter 3.3.1 --- The illegal Constraint --- p.14Chapter 3.3.2 --- The atmost Constraint --- p.14Chapter 3.4 --- General Non-Binary Constraints --- p.15Chapter 3.4.1 --- Constraint Transformation --- p.15Chapter 3.4.2 --- Using the illegal Constraints --- p.17Chapter 3.4.3 --- Problem Transformation --- p.17Chapter 4 --- An Extended GENET --- p.20Chapter 4.1 --- Network Architecture --- p.20Chapter 4.2 --- Convergence Procedure --- p.22Chapter 4.3 --- E-GENET as a Generalization of GENET --- p.24Chapter 4.3.1 --- Constraints --- p.30Chapter 4.3.2 --- Network Architecture --- p.31Chapter 4.4 --- Properties of E-GENET --- p.32Chapter 4.4.1 --- Incompleteness of E-GENET --- p.33Chapter 4.4.2 --- Making E-GENET Complete --- p.36Chapter 4.5 --- Storage Scheme --- p.38Chapter 4.5.1 --- The illegal and atmost Constraint --- p.39Chapter 4.5.2 --- The Disequality Constraint --- p.39Chapter 4.5.3 --- General Constraints --- p.40Chapter 4.6 --- Benchmarking Results --- p.40Chapter 4.6.1 --- The Graph-Coloring Problem --- p.41Chapter 4.6.2 --- The N-queens Problem --- p.42Chapter 4.6.3 --- The Car-Sequencing Problem --- p.43Chapter 4.6.4 --- The Cryptarithmetic Problem --- p.44Chapter 4.6.5 --- The Hamiltonian Path Problem --- p.45Chapter 5 --- Optimizations to E-GENET --- p.47Chapter 5.1 --- Inadequacies of E-GENET --- p.47Chapter 5.1.1 --- Cumbrous Constraint Node --- p.48Chapter 5.1.2 --- Inefficiency of the min-conflicts heuristic --- p.48Chapter 5.2 --- Optimizations --- p.50Chapter 5.2.1 --- Intermediate Node --- p.50Chapter 5.2.2 --- New Assignment Scheme of Initial Penalty Values --- p.55Chapter 5.2.3 --- Concept of Contribution --- p.57Chapter 5.2.4 --- Learning Heuristic --- p.62Chapter 6 --- A Comprehensive Constraint Library --- p.63Chapter 6.1 --- Elementary Constraints --- p.64Chapter 6.1.1 --- Linear Arithmetic Constraints --- p.64Chapter 6.1.2 --- The atmost Constraint --- p.66Chapter 6.1.3 --- Disjunctive Constraints --- p.69Chapter 6.2 --- Global Constraints --- p.71Chapter 6.2.1 --- The cumulative Constraint --- p.72Chapter 6.2.2 --- The among Constraint --- p.77Chapter 6.2.3 --- The diffn Constraint --- p.82Chapter 6.2.4 --- The cycle Constraint --- p.85Chapter 7 --- Conclusion --- p.89Chapter 7.1 --- Contributions --- p.89Chapter 7.2 --- Discussions --- p.90Chapter 7.3 --- Future Work --- p.94Bibliography --- p.9

Increasing The Effectiveness of Deduction in Propositional SAT Solvers

The satisfiability (SAT) of a propositional formula is the decision problem to determine whether there is a satisfying assignment that can make the formula true or not. In the past few years, many successful SAT solvers based on the David-Putnam-Logemann-Loveland (DPLL) procedure [DP60, DLL62, MS99, MMZ+01, ES03] for formulae in conjunctive normal form (CNF) have been developed. Since the deduction procedure of DPLL is sound but not complete, its effects depend on which formula is selected to represent the input function. CNF transformations are among the most effective techniques to improve quality of the input formula by either simplifying clauses [ES03, EB05,|SE05, ZKKSV06, HS07, HS09] or learning new ones [MS99]. Specifically, effective CNF transformations can help SAT solvers to be sped up by allowing them to do more deductions and less enumerations. In my dissertation, I characterize existing transformations in terms of their impact on the deductive power of the formula and their effects on the proof conciseness, that is, the sizes of the implication graphs. I also present two new techniques that try to increase deductive power. The first is a check performed during the computation of resolvents. The second is a new preprocessing algorithm based on distillation that combines simplification and increase of deductive power. Most current SAT solvers apply resolution at various stages to derive new clauses or simplify existing ones. The former happens during conflict analysis, while the latter is usually done during preprocessing. I show how subsumption of the operands by the resolvent can be inexpensively detected during resolution; I then discuss how this detection is used to improve three stages of the SAT solver: variable elimination, clause distillation, and conflict analysis. The on-the-fly subsumption check is easily integrated in a SAT solver. In particular, it is compatible with strong conflict analysis and the generation of unsatisfiability proofs. Experiments show the effectiveness of the new techniques. SAT solvers also benefit from clauses learned by the DPLL procedure, even though they are by definition redundant. In addition to those derived from conflicts, the clauses learned by dominator analysis during the deduction procedure tend to produce smaller implication graphs and sometimes increase the deductive power of the input CNF formula. I extend dominator analysis with an efficient self-subsumption check. I also show how the information collected by dominator analysis can be used to detect redundancies in the satisfied clauses and, more importantly, how it can be used to produce supplemental conflict clauses. I characterize these transformations in terms of deductive power and proof conciseness. Experiments show that the main advantage of dominator analysis and its extensions lies in improving proof conciseness

Formal Verification based on Boolean Expression Diagrams

AbstractThis dissertation examines the use of a new data structure called Boolean Expression Diagrams (BEDs) in the area of formal verification. The recently developed data structure allows fast and efficient manipulation of Boolean formulae. Many problems in formal verification can be cast as problems on Boolean formulae. We chose a number of such problems and show how to solve them using BEDs.Equivalence checking of combinational circuits is a formal verification problem which translates into tautology checking of Boolean formulae. Using BEDs we are able to preserve much of the structure of the circuits within the Boolean formulae. We show how to exploit the structural information in the verification process.Sometimes combinational circuits are specified in a hierarchical or modular way. We present a method for verifying equivalence between two such circuits. The method builds on cut propagation. Assuming that the two circuits are given identical inputs, we propagate this knowledge through the circuits from the inputs to the outputs. The result is the knowledge of how the outputs of the two circuits correspond, e.g., are the outputs of the two circuits pairwise equivalent? The circuits and the movements of cuts can be described using Boolean formulae.Symbolic model checking is a technique for verifying temporal specifications of finite state machines. It is well known how finite state machines and the evaluation of the temporal specifications can be expressed using Boolean formulae. We show how to do these manipulations using BEDs. We concentrate on examples which are hard for standard symbolic model checking methods.Determining whether a formula is satisfiability is a problem which occurs in verification of combinational circuits and in symbolic model checking. Often satisfiability checking is associated with detecting errors. We examine how satisfiability checking can be done using the BED data structure.Finally, we take a look at how it is possible to extend the BED data structure. Among other operations, we introduce an operator for computing minimal p-cuts in fault trees. A fault tree is a Boolean formula expressing whether a system fails based on the condition (“failure” or “working”) of each of the components. A minimal p-cut is a representation of the most likely reasons for system failure. This method can be used to calculate approximately the probability of system failure given the failure probabilities of each of the components.As part of this research, we have developed a BED package. The appendix describes the package from a user's point of view