Skip to main content
SpringerLink
Log in

Privacy sensitive environment re-decomposition for junction tree agent organization construction

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

A number of frameworks for decentralized probabilistic reasoning, constraint reasoning, and decision theoretic reasoning assume a junction tree agent organization (JT-org). A natural decomposition of agent environment may not admit a JT-org. Hence, JT-org construction involves three related tasks: (1) Recognize whether a JT-org exists for a given environment decomposition. (2) When JT-orgs exist, construct one. (3) When no JT-org exists, revise the environment decomposition so that one exists and then construct it. Task 3 requires re-decomposition of the environment. However, re-decomposition incurs loss of JT-org linked privacy, including agent privacy, topology privacy, privacy on private variables, and privacy on shared variables. We propose a novel algorithm suite Distributed Agent Environment Re-decomposition (DAER) that accomplishes all three tasks distributively. For Tasks 1 and 2, DAER incurs no loss of JT-org linked privacy. For Task 3, it incurs significantly less privacy loss than existing JT-org construction methods. Performance of DAER is formally analyzed and empirically evaluated.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Aji, S. M., & McEliece, R. J. (2000). The generalized distributive law. IEEE Transactions Information Theory, 46(2), 325–343.

    Article  MathSciNet  Google Scholar 

  2. Brito, I., & Meseguer, P. (2010). Cluster tree elimination for distributed constraint optimization with quality guarantees. Fundamenta Informaticae, 102(3–4), 263–286.

    Article  MathSciNet  Google Scholar 

  3. Gao, Y., Toni, F., Wang, H., & Xu, F. (2016). Argumentation-based multi-agent decision making with privacy preserved. In J. Thangarajah, K. Tuyls, C. Jonker, & S. Marsella, (Eds.), Proceedings 15th International Conference on Autonomous Agents and Multiagent Systems (pp. 1153–1161).

  4. Golumbic, M. C. (1980). Algorithmic graph theory and perfect graphs. Cambridge: Academic Press.

    MATH  Google Scholar 

  5. Hoang, K. D., Fioretto, F., Hou, P., Yokoo, M., Yeoh, W., & Zivan, R. (2016). Proactive dynamic distributed constraint optimization. In J. Thangarajah, K. Tuyls, C. Jonker, & S. Marsella, (Eds.), Proceedings of 15th international conference on autonomous agents and multiagent systems (pp. 597–605).

  6. Koller, D., & Milch, B. (2001). Multi-agent influence diagrams for representing and solving games. In Proceedings of 17th international joint conference on artificial intelligence (pp. 1027–1034).

  7. Le, T., Fioretto, F., Yeoh, W., Son, T. C., & Pontelli, E. (2016). Er-dcops: A framework for distributed constraint optimization with uncertainty in constraint utilities. In J. Thangarajah, K. Tuyls, C. Jonker, & S. Marsella, (Eds.), Proceedings of 15th international conference on autonomous agents and multiagent systems (pp. 606–614).

  8. Leaute, T., & Faltings, B. (2013). Protecting privacy through fistributed vomputation in multi-agent decision making. Journal of Artificial Intelligence Research, 47, 649–695.

    Article  MathSciNet  Google Scholar 

  9. Maestre, A., & Bessiere, C. (2004). Improving asynchronous backtracking for dealing with complex local problems. In Proceedings of 16th European conference on artificial intelligence (pp. 206–210).

  10. Maheswaran, R. T., Pearce, J. P., Bowring, E., Varakantham, P., & Tambe, M. (2006). Privacy loss in distributed constraint reasoning: A quantitative framework for analysis and its applications. Journal of Autonomous Agents and Multi-Agent Systems, 13(1), 27–60.

    Article  Google Scholar 

  11. Mahmoud, S., Miles, S., & Luck, M. (2016). Cooperation emergence under resource-constrained peer punishment. In J. Thangarajah, K. Tuyls, C. Jonker, and S. Marsella, (Eds.), Proceedings of 15th international conference on autonomous agents and multiagent systems (pp. 900–908).

  12. Modi, P. J., Shen, W., Tambe, M., & Yokoo, M. (2005). Adopt: Asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligences, 161(1–2), 149–180.

    Article  MathSciNet  Google Scholar 

  13. Petcu, A., & Faltings, B. (2005). A scalable method for multiagent constraint optimization. In Proceedings of 19th international joint conference on artificial intelligence (pp. 266–271).

  14. Tassa, T., Grinshpoun, T., & Zivan, R. (2017). Privacy preserving implementation of the Max-Sum algorithm and its variants. Journal of Artificial Intelligence Research, 59, 311–349.

    Article  MathSciNet  Google Scholar 

  15. Valtorta, M., Kim, Y. G., & Vomlel, J. (2002). Soft evidential update for probabilistic multiagent systems. International Journal of Approximate Reasoning, 29(1), 71–106.

    Article  MathSciNet  Google Scholar 

  16. Vinyals, M., Rodriguez-Aguilar, J. A., & Cerquides, J. (2010). Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law. Journal of Autonomous Agents and Multi-Agent Systems, 22(3), 439–464.

    Article  Google Scholar 

  17. Xiang, Y. (1996). A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication. Artificial Intelligence, 87(1–2), 295–342.

    Article  MathSciNet  Google Scholar 

  18. Xiang, Y. (2002). Probabilistic reasoning in multiagent systems: A graphical models approach. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  19. Xiang, Y. (2008). Building intelligent sensor networks with multiagent graphical models. In N. Ichalkaranje, G. P. Wren, & L. C. Jain (Eds.), Intelligent decision making: An AI-based approach (pp. 289–320). Berlin: Springer.

    Chapter  Google Scholar 

  20. Xiang, Y., & Hanshar, F. (2015). Multiagent decision making in collaborative decision networks by utility cluster based partial evaluation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 23(2), 149–191.

    Article  MathSciNet  Google Scholar 

  21. Xiang, Y., & Srinivasan, K. (2016). Privacy preserving existence recognition and construction of hypertree agent organization. Journal of Autonomous Agents and Multi-Agent Systems, 30(2), 220–258.

    Article  Google Scholar 

  22. Yokoo, M., Suzuki, K., & Hirayama, K. (2005). Secure distributed constraint satisfaction: Reaching agreement without revealing private information. Artificial Intelligence, 161, 229–246.

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

Financial supports from the NSERC (Grant No. RGPIN-2017-03715) Discovery Grant to the first author, and the Scholarship from the Saudi Arabian Cultural Bureau to the second author are acknowledged.

Author information

Authors and Affiliations

  1. University of Guelph, Guelph, Canada

    Yang Xiang & Abdulrahman Alshememry

Authors
  1. Yang Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdulrahman Alshememry
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yang Xiang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiang, Y., Alshememry, A. Privacy sensitive environment re-decomposition for junction tree agent organization construction. Auton Agent Multi-Agent Syst 34, 15 (2020). https://doi.org/10.1007/s10458-019-09438-6

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s10458-019-09438-6

Keywords

Search

Navigation