Skip to main content
Log in

A Constraint-Based Approach to Assigning System Components to Tasks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In multi-component systems, individual components must be assigned to the tasks that they are to perform. In many applications, there are several possible task decompositions that could be used to achieve the task, and there are limited resources available throughout the system. We present a technique for making task assignments under these conditions. Constraint satisfaction is used to assign components to particular tasks. Heuristics suggest a task decomposition for which an assignment can be found efficiently. We have applied our technique to the problem of task assignment in systems of underwater robots and instrument platforms working together to collect data in the ocean.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R.G. Smith, “The contract net protocol: High-level communication and control in a distribued problem solver,” IEEE Transactions on Computers, vol. 29,no. 12, pp. 1104–1113, 1980.

    Google Scholar 

  2. S. Cammarata, D. McArthur, and R. Steeb, “Strategies of cooperation in distributed problem solving,” in Proceedings of the 1983 International Joint Conference on Artificial Intelligence, 1983, pp. 767–770.

  3. E.H. Durfee and V.R. Lesser, “Negotiating task decomposition and allocation using partial global planning,” in Distributed Artificial Intelligence, edited by L. Gasser and M.N. Huhns, chap. 10, Morgan Kaufmann Publishers: San Mateo, CA, pp. 229–243, 1989.

    Google Scholar 

  4. T. Sandholm, “An implementation of the contract net protocol based on marginal cost calculations,” in Proceedings of Eleventh National Conference on Artificial Intelligence (AAAI-93), 1993, pp. 262–265.

  5. N. Sadeh and M.S. Fox, “Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem,” Artificial Intelligence, vol. 86,no. 1, pp. 1–41, 1996.

    Google Scholar 

  6. T.B. Curtin, J.G. Bellingham, J. Catipovic, and D. Webb. “Autonomous oceanographic sampling networks,” Oceanography, vol. 6,no. 3, 1993.

  7. V. Kumar, “Algorithms for constraint-satisfaction problems: A survey,” AI Magazine, vol. 13, pp. 32–44, 1992.

    Google Scholar 

  8. M.S. Fox, N. Sadeh, and C. Baykan, “Constrained heuristic search,” in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), 1989.

  9. J.G. Bellingham, H. Schmidt, and C. Chryssostomidis, “AOSN MURI: Real-time oceanography with autonomous oceanographic sampling networks: A center for excellence,” Technical report, MIT Sea Grant, 1997. (Available on the WWW: http://cook.mit.edu/~auvlab/MURI/1997_Rprtfi_nal.html).

  10. J. Bellingham, C. Goudey, T. Consi, J. Bales, and D. Atwood, “A second-generation survey AUV,” in Proceedings of the 1994 IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV'94), Cambridge, MA, USA, July 1994, pp. 148–155.

  11. C. Hewitt, “Offices are open systems,” Communications of the ACM, vol. 4,no. 3, pp. 271–287, 1986.

    Google Scholar 

  12. R. Turner, E. Turner, and D. Blidberg, “Organization and reorganization of autonomous oceanographic sampling networks,” in Proceedings of the 1996 IEEE Symposium on Autonomous Underwater Vehicle Technology, Monterey, CA, June 1996, pp. 407–413.

  13. R.M. Turner and E.H. Turner, “Organization and reorganization of autonomous oceanographic sampling networks,” in Proceedings of the 1998 IEEE International Conference on Robotics and Automation (ICRA'98), Leuven, Belgium, May 1998, pp. 2060–2067.

  14. F. Bub, W. Brown, P. Mupparapu, K. Jacobs, and B. Rogers, “Hydrographics survey report: Convective overturn experiment (CONVEX): R/V endeavor cruise en-291,” Technical report, University of New Hampshire Ocean Process Analysis Laboratory, 1997. (WWW: http://ekman.sr.unh.edu/OPAL/CONVEX/EN291/en291_report.html).

  15. D.S. Weld, “An introduction to least commitment planning,” AI Magazine, pp. 27–61, 1994.

  16. K. Sycara, S. Roth, N. Sadeh, and M. Fox, “Distributed constrained heuristic search,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 21,no. 6, pp. 1446–1461, 1991.

    Google Scholar 

  17. A. Mackworth, “Consistency in networks of relations,” Artificial Intelligence, vol. 8,no. 1, pp. 99–118, 1977.

    Google Scholar 

  18. C.A. Baykan and M.S. Fox, “Constraint techniques for spatial planning,” in Intelligent CAD Systems III: Practical Experience and Evaluation, edited by P. ten Hagen and P.J. Veerkamp, Springer-Verlag: New York, pp. 187–204, 1991.

    Google Scholar 

  19. N. Sathi, M.S. Fox, R. Goyal, and A.S. Kott, “Resource configuration and allocation: A case study of constrained heuristic search,” IEEE Expert, vol. 7,no. 2, pp. 26–35, 1992.

    Google Scholar 

  20. J.C. Giarratano, CLIPS User's Guide, NASA, Information Systems Directorate, Software Technology Branch, Lyndon B. Johnson Space Center, Houston, TX, 1993.

    Google Scholar 

  21. S.D. Anderson, D.L. Westbrook, M. Schmill, A. Carlson, D.M. Hart, and P.R. Cohen, Common Lisp Analytical Statistics Package: User Manual, Department of Computer Science, University of Massachusetts, 1995.

  22. P.R. Cohen, Empirical Methods in Artificial Intelligence, The MIT Press: Cambridge, MA, 1995.

    Google Scholar 

  23. E.H. Turner and R.M. Turner, “A constraint-based approach to assigning system components to tasks,” in Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA-98-AIE), edited by J. Mira, A. del Pobil, and M. Ali, Lecture Notes in Artificial Intelligence 1415: Methodology and Tools in Knowledge-Based Systems, Springer: New York, 1998, vol. I, pp. 312–320.

    Google Scholar 

  24. N. Sadeh, K. Sycara, and Y. Xiong, “Backtracking techniques for the job shop scheduling constraint satisfaction problem,” Artificial Intelligence, vol. 76, pp. 455–480, 1995.

    Google Scholar 

  25. R. Dechter, “Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition,” Artificial Intelligence, vol. 41, pp. 273–312, 1990.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Turner, E.H., Turner, R.M. A Constraint-Based Approach to Assigning System Components to Tasks. Applied Intelligence 10, 155–172 (1999). https://doi.org/10.1023/A:1008371702397

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008371702397

Navigation