Programming Constraint Services

1. Front Matter

   Pages I-XII

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2. Introduction

   Pages 1-7

3. Constraint Programming

   Pages 9-14

4. Introducing Oz Light

   Pages 15-27

5. Spaces for Search

   Pages 29-44

6. Search Engines

   Pages 45-54

7. Best-Solution Search

   Pages 55-58

8. Recomputation

   Pages 59-67

9. Oz Explorer:Visual Search

   Pages 69-78

10. Distributed Search

    Pages 79-91

11. Spaces for Combinators

    Pages 93-104

12. Constraint Combinators

    Pages 105-116

13. Implementing Oz Light

    Pages 117-120

14. Implementing Spaces

    Pages 121-141

15. Other Approaches to Search

    Pages 143-152

16. Conclusion

    Pages 153-156

17. Back Matter

    Pages 157-176

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Constraint Programming is an approach for modeling and solving combi- torial problems that has proven successful in many applications. It builds on techniques developed in Arti?cial Intelligence, Logic Programming, and - erations Research. Key techniques are constraint propagation and heuristic search. Constraint Programming is based on an abstraction that decomposes a problem solver into a reusable constraint engine and a declarative program modeling the problem. The constraint engine implements the required pr- agation and search algorithms. It can be realized as a library for a general purpose programming language (e.g. C++), as an extension of an existing language (e.g. Prolog), or as a system with its own dedicated language. The present book is concerned with the architecture and implementation of constraint engines. It presents a new, concurrent architecture that is far superior to the sequential architecture underlying Prolog. The new archit- ture is based on concurrent search with copying and recomputation rather than sequential search with trailing and backtracking. One advantage of the concurrent approach is that it accommodates any search strategy. Furth- more, it considerably simpli?es the implementation of constraint propagation algorithms since it eliminates the need to account for trailing and backtra- ing. The book investigates an expressive generalization of the concurrent - chitecture that accommodates propagation-preserving combinators (known as deep guard combinators) for negation, disjunction, implication, and re- cation of constraint propagators. Such combinators are beyond the scope of Prolog’s technology. In the concurrent approach they can be obtained with a re?ective encapsulation primitive.

• AI Planning
• Combinatorial Optimization
• Constraint Combinators
• Constraint Logical Programming
• Constraint Technology
• Constraint-Based Modeling
• Constraints
• Constraitn Solving
• Heuristic Search
• Search Strategies
• constraint programming
• programming
• algorithm analysis and problem complexity

• Programming Systems Laboratory, Saarland University, Saarbrücken, Germany

  Christian Schulte

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