A Novel Multi-agent Automated Negotiation Model Based on Associated Intent

  • Weijin Jiang
  • Yusheng Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4088)


With the information explosion speeds up the increasing of com-puting complexity rapidly, the traditional centralized computing patterns are under great pressure to process those large-scale distributed information. However, the agent-based computation and high-level interaction protocols foster the modern computation and distributed information processing successfully. The multi-agent system (MAS) plays an important role in the analysis of the human-interaction theory and model building. This study focuses on the formal description of MAS, the conflict-resolving mechanisms and the negotiation in MAS. The communication between agents has some special requirements. One of them is asynchronous communication. Used communication sequence process (CSP) to descript a model of agents communication with shared buffer channel. The essence of this model is very suitable for the multi-agents communication, so it is a base for our next step job. Based on the communication model, explored the distributed tasks dealing method among joint intention agents and with description of relation between tasks we give a figure of agents’ organization. Agents communicate with each other in this kind of organization. The semantics of agent communication is another emphasis in this paper. With the detailed description of agents’ communication process, given a general agent automated negotiation protocol based on speech act theory in MAS, then we use CSP to verify this protocol has properties of safety and liveness, so prove it is logic right. At last a frame of this protocol’s realization was given.


Agent Communication Asynchronous Communication Communication Sequence Process Negotiation Protocol Automate Negotiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jennings, N.R., Faratin, P., Lomuscio, A.R., et al.: Automated negotiation: prospects. Methods and challenges[C]. In: Pacific Rim International Conference on Artificial Intelligence (2000)Google Scholar
  2. 2.
    Grosz, B., Sidner, C.: Plans for discourse[A]. In: Cohen, P., Morgan, J., Pollack, M. (eds.) Intentions in communication [M], Bradford Books, MIT Press (1990)Google Scholar
  3. 3.
    Bin, W., Yao-xue, Z., Song-qiao, C.: A communication method of MAS based on blackboard architecture[J]. Mini-Micro Systems 23(11), 1355–1358 (2002)Google Scholar
  4. 4.
    Agha, G., Decindio, F. (eds.): Concurrent Object-Oriented Programming and Petri Nets. Lecture notes in Computer Science[M]. Springer, Berlin (1998)Google Scholar
  5. 5.
    Weijin, J.: Modeling and Application of Complex Diagnosis Distributed Intelligence Based on MAS. Journal of Nanjing University (Natural Science) 40(4), 483–496 (2004)Google Scholar
  6. 6.
    Weijin, J.: Research on Diagnosis Model Distributed Intelligence and Key Technique Based on MAS. Journal of Control Theory & Applications 20(6), 231–236 (2004)Google Scholar
  7. 7.
    Wen-pin, J., Zhong-Zhi, S.: Modeling dynamic architectures for multi-agent system[J]. Chinese Journal of Computers 23(7), 732–737 (2000)Google Scholar
  8. 8.
    Xin-jun, M.: Anon-terminating active computing model in multi-agent systems[J]. Journal of Computer Research & Development 36(7), 769–775 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Weijin Jiang
    • 1
  • Yusheng Xu
    • 2
  1. 1.School of computerHunan University of TechnologyZhuzhouP.R. China
  2. 2.College of Applied ElectronicsBeijing University of Science & TechnologyBeijingP.R. China

Personalised recommendations