A practical use of Jackson's preemptive schedule for solving the job shop problem
Annals of Operations Research
Improved CLP scheduling with task intervals
Proceedings of the eleventh international conference on Logic programming
Backtracking techniques for the job shop scheduling constraint satisfaction problem
Artificial Intelligence - Special volume on planning and scheduling
An algorithm to evaluate quantified Boolean formulae
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics
Artificial Intelligence
Constraint-Based Scheduling
An Algorithm to Evaluate Quantified Boolean Formulae and Its Experimental Evaluation
Journal of Automated Reasoning
A New Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem
Proceedings of the 5th International IPCO Conference on Integer Programming and Combinatorial Optimization
Beyond NP: Arc-Consistency for Quantified Constraints
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Time-Indexed Formulations for Machine Scheduling Problems: Column Generation
INFORMS Journal on Computing
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Principles of Constraint Programming
Principles of Constraint Programming
A solver for quantified Boolean and linear constraints
Proceedings of the 2007 ACM symposium on Applied computing
Solving quantified constraint satisfaction problems
Artificial Intelligence
Modeling adversary scheduling with QCSP+
Proceedings of the 2008 ACM symposium on Applied computing
CSP properties for quantified constraints: definitions and complexity
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Clause/term resolution and learning in the evaluation of quantified Boolean formulas
Journal of Artificial Intelligence Research
QCSP made practical by virtue of restricted quantification
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Scenario-based stochastic constraint programming
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Backjumping for quantified Boolean logic satisfiability
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
QCSP-solve: a solver for quantified constraint satisfaction problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Solution directed backjumping for QCSP
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Algorithms for stochastic CSPs
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
BlockSolve: a bottom-up approach for solving quantified CSPs
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Finite domain bounds consistency revisited
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Beyond QCSP for solving control problems
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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The Quantified Constraint Satisfaction Problem (QCSP) extends classical CSP in a way which allows reasoning about uncertainty. In this paper I present novel algorithms for solving QCSP. Firstly I present algorithms to perform constraint propagation on reified disjunction constraints of any length. The algorithms make full use of quantifier information to provide a high level of consistency. Secondly I present a scheme to enforce the non-binary pure value rule. This rule is capable of pruning universal variables. Following this, two problems are modelled in non-binary QCSP: the game of Connect 4, and a variant of job-shop scheduling with uncertainty, in the form of machine faults. The job shop scheduling example incorporates probability bounding of scenarios (such that only fault scenarios above a probability threshold are considered) and optimization of the schedule makespan. These contribute to the art of modelling in QCSP, and are a proof of concept for applying QCSP methods to complex, realistic problems. Both models make use of the reified disjunction constraint, and the non-binary pure value rule. The example problems are used to evaluate the QCSP algorithms presented in this paper, identifying strengths and weaknesses, and to compare them to other QCSP approaches.