Distributed Configuration as Distributed Dynamic Constraint Satisfaction

  • Alexander Felfernig
  • Gerhard Friedrich
  • Dietmar Jannach
  • Markus Zanker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2070)


Dynamic constraint satisfaction problem (DCSP) solving is one of the most important methods for solving various kinds of synthesis tasks, such as configuration. Todays configurators are standalone systems not supporting distributed configuration problem solving functionality. However, supply chain integration of configurable products requires the integration of configuration systems of different manufacturers, which jointly offer product solutions to their customers. As a consequence, we need problem solving methods that enable the computation of such configurations by several distributed configuration agents. Therefore, one possibility is the extension of the configuration problem from a dynamic constraint satisfaction representation to distributed dynamic constraint satisfaction (DDCSP). In this paper we will contribute to this challenge by formalizing the DDCSP and by presenting a complete and sound algorithm for solving distributed dynamic constraint satisfaction prob- lems. This algorithm is based on asynchronous backtracking and enables strategies for exploiting conflicting requirements and design assumptions (i.e. learning additional constraints during search). The exploitation of these additional constraints is of particular interest for configuration be- cause the generation and the exchange of conflicting design assumptions based on nogoods can be easily integrated in existing configuration sys- tems.


Constraint Satisfaction Activity Constraint Agent Variable Supply Chain Integration Connected Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    R. J. Bayardo and D. P. Miranker. A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem. In Proceedings AAAI, pages 298–304, Portland, Oregon, 1996.Google Scholar
  2. 2.
    M. Chandy and L. Lamport. Distributed snapshots: determining global states of distributed systems. ACM Trans. Comput. Syst., 3, 1:63–75, 1985.CrossRefGoogle Scholar
  3. 3.
    R. Weigel D. Sabin. Product Configuration Frameworks-A Survey. In E. Freuder B. Faltings, editor, IEEE Intelligent Systems, Special Issue on Configuration, volume 13, 4, pages 50–58. 1998.Google Scholar
  4. 4.
    T. P. Darr and W. P. Birmingham. An Attribute-Space Representation and Algorithm for Concurrent Engineering. AIEDAM, 10, 1:21–35, 1996.Google Scholar
  5. 5.
    R. Dechter. Enhancements schemes for constraint processing: backjumping, learning and cutset decomposition. Artificial Intelligence, 40, 3:273–312, 1990.CrossRefGoogle Scholar
  6. 6.
    R. Dechter and J. Pearl. Tree clustering for constraint networks. Artificial Intelligence, 38:353–366, 1989.zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    B. Faltings, E. Freuder, and G. Friedrich, editors. Workshop on Configuration. AAAI Technical Report WS-99-05, Orlando, Florida, 1999.Google Scholar
  8. 8.
    A. Felfernig, G. Friedrich, and D. Jannach. UML as domain specific language for the construction of knowledge-based configuration systems. In 11th International Conference on Software Engineering and Knowledge Engineering, pages 337–345, Kaiserslautern, Germany, 1999.Google Scholar
  9. 9.
    G. Fleischanderl, G. Friedrich, A. Haselböck, H. Schreiner, and M. Stumptner. Conguring Large Systems Using Generative Constraint Satisfaction. In E. Freuder B. Faltings, editor, IEEE Intelligent Systems, Special Issue on Configuration, volume 13, 4, pages 59–68. 1998.Google Scholar
  10. 10.
    Y. Hamadi, C. Bessiere, and J. Quinqueton. Backtracking in distributed Constraint Networks. In Proceedings of ECAI 1998, pages 219–223, Brighton, UK, 1998.Google Scholar
  11. 11.
    S. Mittal and B. Falkenhainer. Dynamic Constraint Satisfaction Problems. In Proceedings of AAAI 1990, pages 25–32, Boston, MA, 1990.Google Scholar
  12. 12.
    F. Rossi, C. Petrie, and V. Dhar. On the equivalence of constraint satisfaction problems. In Proceedings of ECAI 1990, Stockholm, Sweden, 1990.Google Scholar
  13. 13.
    T. Soininen, E. Gelle, and I. Niemela. A Fixpoint Definition of Dynamic Constraint Satisfaction. In 5th International Conference on Principles and Practice of Constraint Programming-CP’99, pages 419–433, Alexandria, USA, 1999.Google Scholar
  14. 14.
    M. Yokoo, E. H. Durfee, T. Ishida, and K. Kuwabara. The distributed constraint satisfaction problem. IEEE Transactions on Knowledge and Data Engineering, 10, 5:673–685, 1998.CrossRefGoogle Scholar
  15. 15.
    M. Yokoo and K. Hirayama. Distributed constraint satisfaction algorithm for complex local problems. Proceedings of the 3rd International Conference on Multi-Agent Systems (ICMAS-98), Paris, pages 372–379, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alexander Felfernig
    • 1
  • Gerhard Friedrich
    • 1
  • Dietmar Jannach
    • 1
  • Markus Zanker
    • 1
  1. 1.ProduktionsinformatikInstitut für Wirtschaftsinformatik und AnwendungssystemeKlagenfurtAustria

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