Skip to main content
SpringerLink
Log in

NPCAMSD-agent: a prospective agent model

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Currently, the rigid restrictions on agents with only constrained motivations (corresponding constrained cooperations) cause the high violation rate, low profits and even harm to their reputations without incent motivations in agent mental models, and as a result extremely limit the agent cooperations. To cope with the problems (To incent agents to cooperate with low violation rates and high profits, and to make them more active to cooperate), incent motivations (corresponding incent cooperations) are introduced, 3 kinds of motivations are set forth: the constrained motivations including norm (N), policy (P), and contract (C), the incent motivations including bargain (A), promotion (M), and sticker (S) and the internal motivation desire (D). The NPCAMSD logic of the model is formally depicted, and the semantic, syntax and axiom are analysed. The overcoming of motivation logical omniscience problem is analysed. The two layer motivation conflict resolving method is introduced. The implementation of the model and experiment prove that the mental model has low contract violation rate, high profits in cooperations and works well for the diverse contemporary business cooperations.

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

Similar content being viewed by others

References

  1. Amin, N. (2020). Adjusting the framework of multi-agent systems (MAS) and Internet of Things (IoT) for Smart Power Grids[C]. In 16th International Conference, Special Sessions Distributed Computing and Artificial Intelligence, (pp. 188–191).

  2. Liu, W., Wei, Gu., Sheng, W., et al. (2017). Decentralized multi-agent system-based cooperative frequency control for autonomous microgrids with communication constraints[J]. IEEE Transactions on Sustainable Energy, 5(2), 446–456.

    Article  Google Scholar 

  3. Yang, H., Han, Q.-L., Ge, X., et al. (2020). Fault-tolerant cooperative control of multiagent systems: A survey of trends and methodologies[J]. IEEE Transactions on Industrial Informatics, 16(1), 4–17.

    Article  Google Scholar 

  4. Amine, C., Kurosh, M. (2017). Adaptive motivation system under modular reinforcement learning for agent decision-making modeling of biological regulation[C]. ICCCI: Computational Collective Intelligence, pp. 32–42.

  5. Katy, I.G., Zahra, A., Casey, D., et al. (2020). Mental models of ai agents in a cooperative game setting[C]. In Conference on Human Factors in Computing Systems, (pp. 28–32).

  6. Bratman, M. E. (1987). Intention, plans, and practical reason[M]. Cambridge: Harvard University Press.

    Google Scholar 

  7. Rao, A.S.,Georgeff, M. P. (1991) Modeling rational agents within a bdi-architecture[C]. In Proceeding of the 2nd International Conference on Principles of Knowledge Representation and Reasoning, (pp. 473–484). San Mateo: Morgan Kaufmann Publishers.

  8. Cohen, P. R., & Levesque, H. J. (1990). Intention is choice with commitment[J]. Artificial Intelligence, 42(2–3), 213–261.

    Article  Google Scholar 

  9. Konolige, K.,Pollack, M.E. (1993). A representationalist theory of intention[C]. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, (pp. 390–395).

  10. Hu, S.-L., & Shi, C.-Y. (2006). An improved twin-Subset semantic model for intention of Agent[J]. Journal of Software, 17(003), 396–402.

    Article  Google Scholar 

  11. Hu, S.-L., & Shi, C.-Y. (2000). Agent-BDI logic[J]. Journal of Software, 11(10), 1353–1360.

    Google Scholar 

  12. Kang, X.-Q., & Shi, C.-Y. (1999). Multi-agent interaction based on BDI[J]. Chinese Journal of Computers, 22(11), 1166–1171.

    Google Scholar 

  13. Wang, H., Deng, A.. (2013). Non-revision reasoning with inconsistent ontology[C]. In 10th International Conference on Fuzzy Systems and Knowledge Discovery, (pp. 616–620).

  14. Xu, J.-H., Zhang, W., Lu, H.-M., et al. (2001). an implementing mechanism of agent with personality[J]. Journal of Computer Research and Development, 38(6), 648–652.

    Google Scholar 

  15. Dignum, F., Kinny, D., & Sonenberg, L. (2002). From desires, obligations and norms to goals[J]. Congitive Science Quarterly, 2(3–4), 407–430.

    Google Scholar 

  16. Tufiş, M., Ganascia, J.-G. (2017). A normative extension for the BDI agent model[C]. In 17th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, (pp. 693–703).

  17. Broersen, J., Dastani, M., van der Torre, L. (2003). Bdio-ctl: Obligations and the specification of agent behavior[C]. In Proceedings of IJCAI2003, (pp. 1389–1390).

  18. Liao, B.-S., & Gao, J. (2006). Dynamic self-organizing system supported by PDC-agent[J]. Journal of Computeraided Design & Computer Graphics, 18(002), 217–224.

    Google Scholar 

  19. Paier, M., Dünser, M., Scherngell, T., Martin, S. (2017). Knowledge creation and research policy in science-based industries: An empirical agent-based model[C]. Innovation Networks for Regional Development, pp. 153–183.

  20. Freund, C. (2017). Interactions of Macroprudential and Monetary Policy in an Agent-Based Model of the Housing Market[D]. Kiel: University of Kiel.

    Google Scholar 

  21. Fernando, P.S. (2017). Social norms of cooperation in multiagent systems[C]. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, (pp. 1859–1860).

  22. Hoffenson, S. (2017). Sustainability policy-making as a dynamic, agent-based system of systems[C]. In 12th System of Systems Engineering Conference (SoSE), (pp. 1–6).

  23. Li, J.-X., Mao, X.-J., & Shu, Y. (2007). An object-oriented design model of software agent[J]. Journal of Software, 18(3), 582–591.

    Article  Google Scholar 

  24. Boutilier, C. (1994). Toward a logic for qualitative decision theory[C]. In Principles of Knowledge Representation and Reasoning, (pp. 75–86).

  25. Cholvy, L., & Garion, C. (2001). An attempt to adapt a logic of conditional preferences for reasoning with contrary-to-duties[J]. Fundamenta Informaticae, 48(2), 183–204.

    Google Scholar 

  26. Dignum, V. (2004). A model for organizational interaction: Based on agents, founded in logic[D]. Utrecht: Utrecht university.

    Google Scholar 

  27. Peng, Y.-B., Liao, B.-S., Gao, J., et al. (2008). Extended Agent mental state model——NPCD-Agent. Journal of Zhejiang University Engineering Science, 42(5), 768–773.

    Google Scholar 

  28. Jing, W., Wei, L., Shuang, L. (2018). Agent collaboration in intelligent parking lot systems: dynamic generation of commitment protocol[C]. In International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), (pp. 437–444).

  29. Zhang, Q., Durfee, E. H., & Singh, S. (2020). Semantics and algorithms for trustworthy commitment achievement under model uncertainty[J]. Autonomous Agents and Multi-Agent Systems, 34(1), 29–33.

    Article  Google Scholar 

  30. Zhang, Q., Durfee, E.H., Singh, S. (2018). Challenges in the trustworthy pursuit of maintenance commitments under uncertainty[C]. In Proceedings of the 20th International Trust Workshop Co-located with AAMAS/IJCAI/ECAI/ICML, (pp. 75–86).

  31. Zhou, Y.-M., & Gu, H.-M. (2017). Cooperative manufacturing and assembling supported by CRQAOVTMAgent in open environment[J]. Acta Automatica Sinica, 44(07), 1333–1344.

    Google Scholar 

  32. Skovgaard-Olsen, N. (2017). The problem of logical omniscience, the preface paradox, and doxastic commitments[J]. Synthese, 194(3), 917–939.

    Article  Google Scholar 

  33. Alonso, B., & Alonso, B. (2017). Omniscience and Semantic Information[J]. Manuscrito, 40(4), 77–96.

    Article  Google Scholar 

  34. Davidsson, P., Jacobsson, A. (2007). Aligning models of normative systems and artificial societies: Towards norm-governed behavior in virtual enterprises[C]. Normative Multi-agent Systems, pp. 1–12.

  35. Zhang, J., & Cohen, R. (2007). An incentive mechanism for eliciting fair ratings of sellers in e-marketplaces[C] //Autonomous Agents and Multiagent Systems (pp. 1–3). Honolulu, Hawaii: ACM.

    Google Scholar 

Download references

Acknowledgement

This work is supported by National Science Foundation of Zhejiang Province of China, Grant No: LY20F030002.

Author information

Authors and Affiliations

  1. College of Logistic and Information Engineering, Huzhou Radio and Television University, Zhejiang, 313000, China

    You-ming Zhou

Authors
  1. You-ming Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to You-ming Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Ym. NPCAMSD-agent: a prospective agent model. Telecommun Syst 77, 283–296 (2021). https://doi.org/10.1007/s11235-021-00755-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-021-00755-4

Keywords

Search

Navigation