Adaptive Graph Planning Protocol: An Adaption Approach to Collaboration in Open Multi-agent Systems

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)


The adaptive system requires each agent to provide effective adaptive scheme in runtime according to dynamic changes in the environment. This paper offers an Adaptive Graph Planning Protocol (AGPP) that uses the Goal-Capability-Commitment (GCC) meta-model to dynamically reconstruct. The method uses the concept of capability to represent the executable capabilities possessed by the Agent, and introduces the concept of context state to represent the dynamic environment in the adaptive system. The adaptive graph planning protocol generation method is optimized by calculating the semantic matching degree of the context state. To evaluate the effectiveness of our approach, we provide an experimental scheme based on intelligent robot parking system (IRPS). This scheme verifies the execution time efficiency of this method and the adaptive efficiency of offline in case of emergency.


Open multi-agent system Graph planning Adaptive collaboration Goal-Capability-Commitment model 



Project supported by the National Natural Science Foundation of China under Grant (No. 61502355), supported by Scientific Research Project of Education Department of Hubei Province (No. Q20181508), supported by Graduate Innovative Fund of Wuhan Institute of Technology (No. CX2018203).


  1. 1.
    Gottifredi, S., Tamargo, L.H., García, A.J., Simari, G.R.: Arguing about informant credibility in open multi-agent systems. Artif. Intell. 259, 91–109 (2018). Scholar
  2. 2.
    Wang, D.D., Zhou, Q.H., Zhu, W.: Adaptive event-based consensus of multi-agent systems with general linear dynamics. J. Syst. Sci. Complexity 31(1), 120–129 (2018). Scholar
  3. 3.
    Albrecht, S.V., Stone, P.: Autonomous agents modelling other agents: a comprehensive survey and open problems. Artif. Intell. 258, 66–95 (2018). Scholar
  4. 4.
    Floch, J., Hallsteinsen, S., Stav, E., Eliassen, F., Lund, K., Gjorven, E.: Using architecture models for runtime adaptability. IEEE Softw. 23(2), 62–70 (2006). Scholar
  5. 5.
    Blair, G., Bencomo, N., France, R.B.: Models@run.time. Computer 42(10), 22–27 (2009).
  6. 6.
    Morin, B., Barais, O., Jezequel, J., Fleurey, F., Solberg, A.: Models@run.time to support dynamic adaptation. Computer, 42(10), 44–51 (2009).
  7. 7.
    Liu, W., Li, S., Wang, J.: Goal-capability-commitment based context-aware collaborative adaptive diagnosis and compensation. In: Cong Vinh, P., Ha Huy Cuong, N., Vassev, E. (eds.) ICCASA/ICTCC -2017. LNICST, vol. 217, pp. 79–89. Springer, Cham (2018). Scholar
  8. 8.
    Günay, A., Winikoff, M., Yolum, P.: Dynamically generated commitment protocols in open systems. Auton. Agent. Multi-Agent Syst. 29(2), 192–229 (2015). Scholar
  9. 9.
    Krupitzer, C., Roth, F.M., VanSyckel, S.: A survey on engineering approaches for self-adaptive systems. Pervasive Mob. Comput. 17, 184–206 (2015). Scholar
  10. 10.
    Zhao, T.Q., Zhao, H.Y., Zhang, W., Jin, Z.: Survey of model-based self-adaptation methods. J. Softw. 29(1), 23–41 (2018)Google Scholar
  11. 11.
    Liu, H.Z., Bao, H., Xu, D.: Concept vector for similarity measurement based on hierarchical domain structure. Comput. Inform. 30(5), 881–900 (2012)zbMATHGoogle Scholar
  12. 12.
    Farias, T.M.D., Roxin, A., Nicolle, C.: SWRL rule-selection methodology for ontology interoperability. Data Knowl. Eng. 105, 53–72 (2016). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringWuhan Institute of TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent RobotWuhanChina
  3. 3.Enshi No. 1 Senior Middle School of HubeiEnshiChina

Personalised recommendations