Applied Intelligence

, Volume 33, Issue 2, pp 132–143 | Cite as

A multiagent framework for coordinated parallel problem solving

  • Pinar Öztürk
  • Kari Rossland
  • Odd Erik Gundersen


Today’s organizations, under increasing pressure on the effectiveness and the increasing need for dealing with complex tasks beyond a single individual’s capabilities, need technological support in managing complex tasks that involve highly distributed and heterogeneous information sources and several actors. This paper describes CoPSF, a multiagent system middle-ware that simplifies the development of coordinated problem solving applications while ensuring standard compliance through a set of system services and agents. CoPSF hosts and serves multiple concurrent teams of problem solving contributing both to the limitation of communication overheads and to the reduction of redundant work across teams and organizations. The framework employs (i) an interleaved task decomposition and allocation approach, (ii) a mechanism for coordination of agents’ work, and (iii) a mechanism that enables synergy between parallel teams.


Coordinated problem solving Multiagent systems Team formation Team coordination Parallel teams 


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  1. 1.
    Aamodt A (1991) A knowledge-intensive, integrated approach to problem solving and sustained learning. PhD thesis, University of Trondheim, Norway, May 1991 Google Scholar
  2. 2.
    Abasolo C, Arcos J-L, Armengol E, Gomez M, Lopes de Mantaras R, Plaza E (2003) Component selection. In: IBROW deliverable D8, IIIA, Barcelona, January 2003 Google Scholar
  3. 3.
    Bose U (1999) A cooperative problem solving framework for computer-aided process planning. In: HICSS ’99: Proceedings of the thirty-second annual Hawaii international conference on system sciences, vol 8, p 8015 Google Scholar
  4. 4.
    Breuker J, Van de Velde W (eds) (1994) CommonKADS library for expertise modeling: reusable problem solving components. IOS Press, Ohmsha MATHGoogle Scholar
  5. 5.
    Carver N, Lesser V (eds) (1992) The evolution of blackboard control architectures. Tech Report UM-CS-1992-071 Google Scholar
  6. 6.
    Chandrasekaran B, Johnson TR (1993) Generic tasks and task structures: History, critique and new directions. In: David J-M, Krivine J-P, Simmons R (eds) Second generation expert systems. Springer, Berlin, pp 232–272 Google Scholar
  7. 7.
    Corkill DD (1991) Blackboard systems. AI Expert 6(9):40–47 Google Scholar
  8. 8.
    Decker KS (1995) TAEMS: A framework for analysis and design of coordination mechanisms. In: O’Hare G, Jennings N (eds) Foundations of distributed artificial intelligence. Wiley Inter-Science, New York Google Scholar
  9. 9.
    Demazeau Y, Boissier O, Koning JL Using interaction protocols to control vision systems. In: IEEE international conference on systems, man, and cybernetics, vol 2, pp 1616–1621 Google Scholar
  10. 10.
    Dresner K, Stone P (2008) A multiagent approach to autonomous intersection management. J Artif Intell Res 31:591–656 Google Scholar
  11. 11.
    Durfee EH (1999) Distributed problem solving and planning. In: Wei G (ed) Multiagent systems. MIT Press, Cambridge, pp 121–164 Google Scholar
  12. 12.
    Erman L, Hayes-Roth F, Lesser VR, Reddy DR (1980) The Hearsay-II speech-understanding system: Integrating knowledge to resolve uncertainty. ACM Comput Surv 12(2):213–253 CrossRefGoogle Scholar
  13. 13.
    Gomez M, Plaza E, Abasolo C (2002) Problem-solving methods and cooperative information agents. Int J Coop Inf Syst 11(3–4):329–354 CrossRefGoogle Scholar
  14. 14.
    Huang J, Pearce AR (2006) Distributed interactive learning in multi-agent systems. In: Proceedings of the twenty-first national conference on artificial intelligence, pp 666–671 Google Scholar
  15. 15.
    Jennings N (1995) Controlling cooperative problem solving in industrial multi-agent systems using joint intentions. Artif Intell 75(2):195–240 CrossRefGoogle Scholar
  16. 16.
    Jung D, Cheng G, Zelinsky A (1997) An experiment in realising cooperation between autonomous mobile robots. In: Fifth international symposium on experimental robotics (ISER) Google Scholar
  17. 17.
    Lesser VR, Corkill DD (1983) The distributed vehicle monitoring testbed: A tool for investigating distributed problem-solving networks. AI Mag 4(3):15–33 Google Scholar
  18. 18.
    Martin P (2001) Large-scale cooperatively-built heterogeneous KBs. In: Proceedings of the 9th international conference on conceptual structures Google Scholar
  19. 19.
    Steels L (1990) Components of expertise. AI Mag 11(2) Google Scholar
  20. 20.
    Surowiecki J (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations doubleday Google Scholar
  21. 21.
    Sycara K (2003) The RETSINA MAS, a case study. In: Garcia O et al. (eds) Software engineering for large scale multi-agent systems: research issues and practical applications. Springer, New York, pp 232–250 CrossRefGoogle Scholar
  22. 22.
    Ozturk P, Gundersen OE (2004) A combined top-down and bottom-up approach to integrated task-decomposition and allocation. In: The 3rd international conference on machine learning and cybernetics (ICMLC 2004), vol 1. IEEE Press, New York, pp 163–168 Google Scholar
  23. 23.
    Parker LE (2008) Distributed intelligence: Overview of the field and its application in multi-robot systems. J Phys Agents 2(1):5–14 Google Scholar
  24. 24.
    Rosenschein JS, Zlotkin G (1994) Rules of encounter: Designing conventions for automated negotiation among computers. MIT Press, Cambridge Google Scholar
  25. 25.
    Tambe M (1997) Agent architectures for flexible, practical teamwork. National conference on AI (AAAI97), pp 22–28 Google Scholar
  26. 26.
    Tu SW, Eriksson H, Gennari JH, Shahar Y, Musen MA (1995) Ontology-based configuration of problem-solving methods and generation of knowledge acquisition tools: Application of PROTEGE-II to protocol-based decision support. AI Med 7(3):257–289 Google Scholar
  27. 27.
    Xu Y, Liao E, Scerri P, Yu B, Lewis M, Sycara K (2005) Towards flexible coordination of large scale multiagent systems. In: Challenges of large scale coordination. Springer, New York Google Scholar
  28. 28.
    Yousfi F, Bricon-Souf N, Beuscart R, Geib JM PLACO: A cooperative architecture for solving coordination problem in health care, engineering in medicine and biology society, 1995, IEEE 17th annual conference, vol 1, Issue 20–25, Sep 1995, pp 747–748 Google Scholar
  29. 29.
    Zhang Z, Zhang C (2004) Agent-based hybrid intelligent systems: An agent-based framework for complex problem solving. Lecture notes in computer science, vol 2938. Springer, Berlin Google Scholar
  30. 30.
    Zheng Q, Zhang X (2005) Automatic formation and analysis of multi-agent virtual organizations. J Braz Comput Soc 11(1):74–89. Special issue on agents organizations Google Scholar
  31. 31.
  32. 32.

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Pinar Öztürk
    • 1
  • Kari Rossland
    • 2
  • Odd Erik Gundersen
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and Technology (NTNU)TrondheimNorway
  2. 2.Steria ASOsloNorway

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