Knowledge-Based Algorithms for BDI Agents | Automatic Control and Computer Sciences Skip to main content
Springer Nature Link
Log in

Knowledge-Based Algorithms for BDI Agents

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract—

A multiagent algorithm is a knowledge-based distributed algorithm that solves some problem via cooperative work of agents. A BDI agent is an agent with beliefs, desires, and intentions. From the point of view of an individual agent, a multiagent algorithm is an algorithm based on this agent’s knowledge and beliefs, aimed at achieving this agent’s desires by sequentially acting on its intentions. We consider agents to be reactive, proactive, and rational. This paper proposes and studies two knowledge-based multiagent algorithms. In particular, one of the proposed algorithms solves the following resource renting problem: there is a system of agents that arrive at a resource center one by one, in random order, with the goal to rent some available resource. The available resources are passive and form a cloud. If there is no competition for an available resource, it is lend on demand and is returned to the cloud after use. The agents also form a cloud, but leave the resource center immediately after renting the desired resource. The goal is to design a knowledge-based multiagent algorithm with the following correctness property: each agent that arrives at the resource center eventually rents some desired resource at a moment when there is no competition for that resource.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

REFERENCES

  1. Takada, M., Distributed Systems: For Fun and Profit, 2013. http://book.mixu.net/distsys/.

  2. Tanenbaum, A. and van Steen, M., Distributed Systems: Principles and Paradigms, Prentice-Hall, 2006.

    MATH  Google Scholar 

  3. Wooldridge, M., An Introduction to Multiagent Systems, John Willey & Sons, 2002.

    Google Scholar 

  4. Chappell, C., Plato on knowledge in the Theaetetus, in Stanford Encyclopedia of Philosophy, 2019. http://plato.stanford.edu/entries/plato-theaetetus/.

  5. Ichikawa, J. and Steup, M., The analysis of knowledge, in Stanford Encyclopedia of Philosophy, 2017. http://plato.stanford.edu/entries/knowledge-analysis/.

  6. Dütting, P. and Geiger, A., Algorithmic Mechanism Design. Seminar Report, Univ. of Karlsruhe, 2007. https://webspace.science.uu.nl/~leeuw112/msagi/mech_design.pdf.

  7. Fagin, R., Halpern, J., Moses, Y., and Vardi, M., Reasoning about Knowledge, MIT Press, 1995.

    MATH  Google Scholar 

  8. Mell, P. and Grance, T., The NIST Definition of Cloud Computing, NIST Special Publication 800-145, 2011. http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf.

  9. Manna, Z. and Pnueli, A., The Temporal Logic of Reactive and Concurrent Systems: Specification, Springer, 2012.

    MATH  Google Scholar 

  10. Satekbayeva, A. and Shilov, N., Some results on multiagent algorithms in social computing/software context, Information, 2014, vol. 17, no. 1, pp. 229–240.

    Google Scholar 

  11. van Benthem, J., Logical Dynamics of Information and Interaction, Cambridge University Press, 2011.

    Book  Google Scholar 

  12. Alechina, N. and Logan, B., State of the art in logics for verification of resource-bounded multi-agent systems, in Fields of Logic and Computation III — Essays Dedicated to Yuri Gurevich on the Occasion of His 80th Birthday, Springer, 2020, pp. 9–29.

    Google Scholar 

Download references

Funding

This research has been financially supported by the Ministry of Digital Development, Communications and Mass Media of the Russian Federation and the Russian Venture Company (agreement no. 004/20 dated 20.03.2020, IGK 0000000007119P190002).

Author information

Authors and Affiliations

  1. Innopolis University, 420500, Innopolis, Russia

    N. V. Shilov

  2. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences, 630090, Novosibirsk, Russia

    N. O. Garanina

Authors
  1. N. V. Shilov
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. N. O. Garanina
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to N. V. Shilov or N. O. Garanina.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by A. Ovchinnikova

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shilov, N.V., Garanina, N.O. Knowledge-Based Algorithms for BDI Agents. Aut. Control Comp. Sci. 55, 786–794 (2021). https://doi.org/10.3103/S0146411621070129

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411621070129

Keywords: