Science China Technological Sciences

, Volume 62, Issue 1, pp 133–143 | Cite as

Geometry-based distributed arc-consistency method for multiagent planning and scheduling

  • Rui Xu
  • ZhaoYu LiEmail author
  • PingYuan Cui


This research focuses on building a distributed algorithm for planning and scheduling multiple agents to help people deal with events beyond their cognitive capacity, such as car assembly, factory management, spacecraft constellation, etc. We address not only the efficiency of the algorithm but also communication and the individual privacy. As to reason over the problems with multiple agents which are distributed but interconnected, a formal account of the Action-centric Multiagent Simple Temporal Problem (AMSTP) is put forward using the representation of geometries. The key technique we build on is a novel distributed arc-consistency algorithm centered by the geometric method called GDAC, which pays attention to how an agent’s local subproblem affects other agents’ subproblems. The GDAC is based on geometries taking the action rather than the timepoint as a variable, which can deal with continuous intervals and decrease the number of variables. Comprehensive experiments are run and the proposed technique outperforms the competitor and shows considerable merit compared to the centralized algorithm.


arc consistency multiple agents planning and scheduling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wooldridge M. An Introduction to Multiagent Systems. 2nd ed. Hoboken: John Wiley & Sons Ltd, 2009. 1–99Google Scholar
  2. 2.
    Leitão P, Mařík V, Vrba P. Past, present, and future of industrial agent applications. IEEE Trans Ind Inf, 2013, 9: 2360–2372CrossRefGoogle Scholar
  3. 3.
    Fang H, Lu S L, Chen J. New advances in complex motion control for single robot systems and multi-agent systems. Sci China Tech Sci, 2016, 59: 1963–1964CrossRefGoogle Scholar
  4. 4.
    Balaji P G, Srinivasan D. An introduction to multi-agent systems. In: Srinivasan D, Jain L C, eds. Innovations in Multi-Agent Systems and Applications-1. Berlin: Springer, 2010. 1–27Google Scholar
  5. 5.
    Tožička J, Jakubův J, Komenda A, et al. Privacy-concerned multiagent planning. Knowl Inf Syst, 2016, 48: 581–618CrossRefGoogle Scholar
  6. 6.
    Wilcox R, Shah J. Optimization of multi-agent workflow for humanrobot collaboration in assembly manufacturing. In: Infotech@aerospace. Reston: American Institute of Aeronautics and Astronautics, 2013Google Scholar
  7. 7.
    Giordani S, Lujak M, Martinelli F. A distributed multi-agent production planning and scheduling framework for mobile robots. Comp Industrial Eng, 2013, 64: 19–30CrossRefGoogle Scholar
  8. 8.
    Bu H J, Zhang J, Luo Y Z, et al. Multi-objective optimization of space station short-term mission planning. Sci China Tech Sci, 2015, 58: 2169–2185CrossRefGoogle Scholar
  9. 9.
    Mishra N, Singh A, Kumari S, et al. Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing. Int J Prod Res, 2016, 54: 7115–7128CrossRefGoogle Scholar
  10. 10.
    De Weerdt M, Clement B. Introduction to planning in multiagent systems. Multiagent Grid Syst, 2009, 5: 345–355CrossRefGoogle Scholar
  11. 11.
    Martin S, Ouelhadj D, Beullens P, et al. A multi-agent based cooperative approach to scheduling and routing. Eur J Oper Res, 2016, 254: 169–178MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Boerkoel J, Durfee E H. Distributed reasoning for multiagent simple temporal problems. J Artif Intell Res, 2013, 47: 95–156MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Bulling N. A survey of multi-agent decision making. Künstl Intell, 2014, 28: 147–158CrossRefGoogle Scholar
  14. 14.
    Kraus S. Negotiation and cooperation in multi-agent environments. Artif Intell, 1997, 94: 79–97CrossRefzbMATHGoogle Scholar
  15. 15.
    Barták R, Morris R A, Venable K B. An introduction to constraintbased temporal reasoning. In: Brachman R, Stone P, eds. Synthesis Lectures on Artificial Intelligence and Machine Learning. San Francisco: Morgan & Claypool Publishers, 2014, 8: 1–121CrossRefzbMATHGoogle Scholar
  16. 16.
    Sultanik E, Modi P J, Regli W C. On modeling multiagent task scheduling as a distributed constraint optimization problem. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), 2007. 1531–1536Google Scholar
  17. 17.
    Dechter R, Meiri I, Pearl J. Temporal constraint networks. Artif Intell, 1991, 49: 61–95MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Planken L, De Weerdt M M, Van Der Krogt R P J. Computing all-pairs shortest paths by leveraging low treewidth. J Artif Intell Res, 2012, 43: 353–388MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Kautz H. Constraint propagation algorithms for temporal reasoning: A revised report. In: Weld D S, De Kleer J, eds. Readings in Qualitative Reasoning about Physical Systems. San Francisco: Morgan Kaufmann Publishers, 2013. 373–381Google Scholar
  20. 20.
    De Antoni V, Moreira A. An asynchronous algorithm to improve scheduling quality in the multiagent simple temporal problem. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, 2014. 1381–1382Google Scholar
  21. 21.
    Boerkoel Jr J C, Durfee E H. A comparison of algorithms for solving the multiagent simple temporal problem. In: Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS), 2010. 26–33Google Scholar
  22. 22.
    Boerkoel Jr J C, Planken L R, Wilcox R J, et al. Distributed algorithms for incrementally maintaining multiagent simple temporal networks. In: International Conference on Automated Planning and Scheduling, 2013. 11–19Google Scholar
  23. 23.
    Chen H, Dalmau V, Grussien B. Arc consistency and friends. J Log Comput, 2011, 23: 87–108MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Mackworth A K. Consistency in networks of relations. Artif Intell, 1977, 8: 99–118CrossRefzbMATHGoogle Scholar
  25. 25.
    Berkholz C, Verbitsky O. On the speed of constraint propagation and the time complexity of arc consistency testing. Journal of Computer and System Sciences. In: Chatterjee K, Sgall J, eds. International Symposium on Mathematical Foundations of Computer Science. Heidelberg: Springer, 2013. 104–114Google Scholar
  26. 26.
    Bessiére C, Freuder E C, Regin J C. Using constraint metaknowledge to reduce arc consistency computation. Artif Intell, 1999, 107: 125–148MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Ringwelski G. An arc-consistency algorithm for dynamic and distributed constraint satisfaction problems. Artif Intell Rev, 2005, 24: 431–454CrossRefGoogle Scholar
  28. 28.
    Lee D A J. Hybrid algorithms for distributed constraint satisfaction. Dissertation for the Doctoral Degree. Aberdeen: Robert Gordon University, 2010. 1–75Google Scholar
  29. 29.
    Hassine A B, Ghedira K. How to establish arc-consistency by reactive agents. In: Proceedings 15th European Conference on Artificial Intelligence, 2002. 156–160Google Scholar
  30. 30.
    Rit J F. Propagating temporal constraints for scheduling. In: Proceedings 5th National Conference on Artificial Intelligence (AAAI-86). Los Altos: Morgann Kaufmann, 1986. 383–388Google Scholar
  31. 31.
    Li Z, Xu R, Cui P, et al. Geometry-based propagation of temporal constraints. Inf Syst Front, 2017, 19: 855–868CrossRefGoogle Scholar
  32. 32.
    Hunsberger L. Algorithms for a temporal decoupling problem in multi-agent planning. In: Proceedings of the eighteenth National Conference on Artificial Intelligence, 2002. 468–475Google Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Deep Space Exploration Technology, School of Aerospace EngineeringBeijing Institute of TechnologyBeijingChina
  2. 2.Key Laboratory of Autonomous Navigation and Control for Deep Space ExplorationMinistry of Industry and Information TechnologyBeijingChina

Personalised recommendations