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The First Workshop on the Principles and Practice of Constraint Programming (April 28-30, 1993 in Newport, RI) is an inter-disciplinary meeting focusing on constraint programming as a general paradigm for computation.

The First Workshop on the Principles and Practice of Constraint Programming (April 28-30, 1993 in Newport, RI) is an inter-disciplinary meeting focusing on constraint programming as a general paradigm for computation.

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Constraint programming (CP) is a general and powerful method to solve somecombinatorial problems. This method has been successfully used to solve a large range of real-life applications(rostering, time-tabling, car manufacturing, scheduling etc...), which can be quite different. CP is on the borderline of AI and OR.CP inherits from IA the language aspect (that is the easy way to define a problem), the flexibility(that is the fact that problems can be easily modified) and the possibility to benefit from the knowledgeof the application domain to improve the resolution of a problem. Operational Research brings to CP a lot of nice andpowerful algorithms which gives to CP the computational aspect needed to solve some real world problems. In this talk we will introduce the general principles of this method. We willinsist on its originality and on its flexibility. We will show its strength and its weakness.Notably, we will detail how some OR algorithms are used and why no restriction is made on the type

Partial contents: Entailment and Disentailment of Order-Sorted Feature Constraints; Using a Visual Constraint Language for Data Display Specification; The Geometry in Constraint Logic Programs; On the Semantics of Optimization Predicates in CLP Languages; A Higher-Order Extension of Constraint Programming in Discourse Analysis; Practical Issues in Programming Constraints; Memorization in Constraint Logic Programming; Exploiting Constraint Dependency Information For Debugging and Explanation.

The proceedings of the conference 'Principles and Practice of Constraint Programming -- CP 96' are attached. Partial Contents include: (1) An Empirical Study of Dynamic Variable Ordering Heuristics for the Constraint Satisfaction Problem; (2) Empirical Studies of Heuristic Local Search for Constraint Solving; (3) Inference Duality as a Basis for Sensitivity Analysis; (4) Transformation Between HCLP and PCSP; (5) A New Approach for Weighted Constraint Satisfaction: Theoretical and Computational Results; (6) Towards a More Efficient Stochastic Constraint Solver; (7) A View of Local Search in Constraint Programming; (8) From Quasi-Solutions to Solution: An Evolutionary Algorithm to Solve CSP; (9) Logical Semantics of Concurrent Constraint Programming; (10) Constraint Logic Programming over Unions of Constraint Theories; (11) On Query Languages for Linear Queries Definable with Polynomial Constraints; (12) Analysis of Heuristic Methods for Partial Constraint Satisfaction Problems; (13) Solving Satisfiability Problems Using Field Programmable Gate Arrays: First Results; and (14) A Constraint Program for Solving the Job-Shop Problem

The proceedings of the conference 'Principles and Practice of Constraint Programming -- CP 96' are attached. Partial Contents include: (1) An Empirical Study of Dynamic Variable Ordering Heuristics for the Constraint Satisfaction Problem; (2) Empirical Studies of Heuristic Local Search for Constraint Solving; (3) Inference Duality as a Basis for Sensitivity Analysis; (4) Transformation Between HCLP and PCSP; (5) A New Approach for Weighted Constraint Satisfaction: Theoretical and Computational Results; (6) Towards a More Efficient Stochastic Constraint Solver; (7) A View of Local Search in Constraint Programming; (8) From Quasi-Solutions to Solution: An Evolutionary Algorithm to Solve CSP; (9) Logical Semantics of Concurrent Constraint Programming; (10) Constraint Logic Programming over Unions of Constraint Theories; (11) On Query Languages for Linear Queries Definable with Polynomial Constraints; (12) Analysis of Heuristic Methods for Partial Constraint Satisfaction Problems; (13) Solving Satisfiability Problems Using Field Programmable Gate Arrays: First Results; and (14) A Constraint Program for Solving the Job-Shop Problem

The proceedings of the conference 'Principles and Practice of Constraint Programming -- CP 96' are attached. Partial Contents include: (1) An Empirical Study of Dynamic Variable Ordering Heuristics for the Constraint Satisfaction Problem; (2) Empirical Studies of Heuristic Local Search for Constraint Solving; (3) Inference Duality as a Basis for Sensitivity Analysis; (4) Transformation Between HCLP and PCSP; (5) A New Approach for Weighted Constraint Satisfaction: Theoretical and Computational Results; (6) Towards a More Efficient Stochastic Constraint Solver; (7) A View of Local Search in Constraint Programming; (8) From Quasi-Solutions to Solution: An Evolutionary Algorithm to Solve CSP; (9) Logical Semantics of Concurrent Constraint Programming; (10) Constraint Logic Programming over Unions of Constraint Theories; (11) On Query Languages for Linear Queries Definable with Polynomial Constraints; (12) Analysis of Heuristic Methods for Partial Constraint Satisfaction Problems; (13) Solving Satisfiability Problems Using Field Programmable Gate Arrays: First Results; and (14) A Constraint Program for Solving the Job-Shop Problem