An Improved Multi-agent Epistemic Planner via Higher-Order Belief Change Based on Heuristic Search

  • Zhongbin WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)


Recently, multi-agent epistemic planning has drawn attention from both dynamic logic and planning communities. Existing implementations are based on compilation into classical planning, which suffers from limitations such as incapability to handle disjunctive beliefs, or higher-order belief change and forward state space search, as exploited by the planner MEPK. However, MEPK does not scale well. In this paper, we propose two improvements for MEPK. Firstly, we exploit another normal form for multi-agent KD45, which is more space efficient than the normal form used by MEPK, and propose efficient reasoning, revision, and update algorithms for it. Secondly, we propose a heuristic function for multi-agent epistemic planning, and apply heuristic search algorithm AO* with cycle checking and two heuristic pruning strategies. We implement a multi-agent epistemic planner called MEPL. Our experimental results show that MEPL outperforms MEPK in most planning instances, and solves a number of instances which MEPK cannot solve.


Modal logic Multi-agent epistemic planning Heuristic search 



We acknowledge support from the Natural Science Foundation of China under Grant Nos. 61572535.


  1. 1.
    Bolander, T., Andersen, M.B.: Epistemic planning for single and multi-agent systems. J. Appl. Non-Class. Log. 21(1), 9–34 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Van Ditmarsch, H., van Der Hoek, W., Kooi, B.: Dynamic Epistemic Logic. Springer, Heidelberg (2007). Scholar
  3. 3.
    Kominis, F., Geffner, H.: Beliefs in multiagent planning: from one agent to many. In: Proceedings of the 25th ICAPS, 7–11 June 2015, Jerusalem, Israel, pp. 147–155 (2015)Google Scholar
  4. 4.
    Muise, C.J., et al.: Planning over multi-agent epistemic states: a classical planning approach. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25–30 January 2015, Austin, Texas, USA, pp. 3327–3334 (2015)Google Scholar
  5. 5.
    Huang, X., Fang, B., Wan, H., Liu, Y.: A general multi-agent epistemic planner based on higher-order belief change. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. IJCAI-17, pp. 1093–1101 (2017)Google Scholar
  6. 6.
    Fagin, R., Halpern, J.Y., Moses, Y., Vardi, M.: Reasoning About Knowledge. MIT Press, Cambridge (1995)zbMATHGoogle Scholar
  7. 7.
    Hales, J., French, T., Davies, R.: Refinement quantified logics of knowledge and belief for multiple agentsc. In: Advances in Modal Logic 9, Papers From the Ninth Conference on “Advances in Modal Logic,” 22–25 August 2012, Copenhagen, Denmark, pp. 317–338 (2012)Google Scholar
  8. 8.
    Alchourrón, C.E., Gärdenfors, P., Makinson, D.: On the logic of theory change: partial meet contraction and revision functions. J. Symb. Log. 50(2), 510–530 (1985)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Katsuno, H., Mendelzon, A.O.: On the difference between updating a knowledge base and revising it. In: Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning, Cambridge, MA, USA, pp. 387–394 (1991)Google Scholar
  10. 10.
    Darwiche, A., Pearl, J.: On the logic of iterated belief revision. Artif. Intell. 89(1–2), 1–29 (1997)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Satoh, K.: Nonmonotonic reasoning by minimal belief revision. In: FGCS, pp. 455–462 (1988)Google Scholar
  12. 12.
    Winslett, M.: Reasoning about action using a possible models approach. In: Proceedings of the 7th National Conference on Artificial Intelligence, 21–26 August 1988, St. Paul, MN, pp. 89–93 (1988)Google Scholar
  13. 13.
    Hansen, E.A., Zilberstein, S.: Lao\(^*\): a heuristic search algorithm that finds solutions with loops. Artif. Intell. 129(1–2), 35–62 (2001)MathSciNetCrossRefGoogle Scholar
  14. 14.
    McDermott, D., et al.: PDDL-the planning domain definition language. Technical report CVC TR98003/DCS TR1165, Yale Center for Computational Vision and Control, New Haven, CT (1998)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceSun Yat-sen UniversityGuangzhouChina

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