SpringerLink
Log in

Basic Trends of Decentralized Artificial Intelligence

  • SELECTED CONFERENCE PAPERS
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

Analysis of modern trends, advanced concepts, and projects in the field of information technologies shows the growing role of artificial intelligence, primarily decentralized artificial intelligence. The paper analyzes the advanced concepts of building new generation applications in the field of information technologies and relevant applied developments and provides a brief analysis of modern achievements in the field of decentralized artificial intelligence and self-organization, which are able to support the practical implementation of such applications. A simplified analog of the roadmap for the development of decentralized and self-organizing artificial intelligence for the near future has also been formulated.

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

Access this article

Log in via an institution

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. An example of NFT is digital art. Beeple’s digital work, 5000 days, was sold at auction for 69 million dollars; see https://www.heverge.com/2021/3/11/22325054/beeple-christies-nft-sale-cost-everydays-69-million.

  2. FIPA—Foundation for Intelligent Physical Agents, http://www.fipa.org/.

REFERENCES

  1. H. Abelson, D. Allen, D. Coore, C. Hanson, G. Homsy, T. Knight, R. Nagpal, E. Rauch, G. Sussman, and R. Weiss, “Amorphous computing,” Commun. ACM 43 (5), 74–82 (2000). https://doi.org/10.1145/332833.332842

    Article  Google Scholar 

  2. Agent Mining Special Interest Group. http://www.agentmining.org.

  3. M. N. M. Bhutta, A. A. Khwaja, A. Nadeem, H. F. Ahmad, M. Kh. Khan, M. A. Hanif, H. Song, M. Alshamari, and Yu. Cao, “Survey on blockchain technology: Evolution, architecture and security,” IEEE Access 10, 61048–61073 (2021). https://doi.org/10.1109/ACCESS.2021.3072849

    Article  Google Scholar 

  4. M. Camurri, M. Mamei, and F. Zambonelli, “Urban traffic control with co-fields,” in Environments for Multi-agent Systems III, Ed. by D. Weyns, H. V. D. Parunak, and F. Michel, Lecture Notes in Artificial Intelligence, Vol. 4389 (Springer, Berlin, 2007), pp. 239–253. https://doi.org/10.1007/978-3-540-71103-2_14

  5. L. Cao, “Decentralized AI: Edge intelligence and smart blockchain, metaverse, Web3, and DeSci,” IEEE Intell. Syst. 37 (3), 6–19 (2022). https://doi.org/10.1109/mis.2022.3181504

    Article  Google Scholar 

  6. L. Cao, V. Gorodetsky, and P. A. Mitkas, “Agent mining: The synergy of agents and data mining,” IEEE Intell. Syst. 24 (3) (2009). https://doi.org/10.1109/MIS.2009.45

  7. L. Cao, G. Weiss, and P. S. Yu, “A brief introduction to agent mining,” Auton. Agents Multi-Agent Syst. 25, 419–424 (2012). https://doi.org/10.1007/s10458-011-9191-4

    Article  Google Scholar 

  8. Ph. K. Chan, W. Fan, and S. J. Stolfo, “Distributed data mining in credit card fraud detection,” IEEE Intell. Syst. 14 (6), 67–74 (1999). https://doi.org/10.1109/5254.809570

    Article  Google Scholar 

  9. P. Cohen and H. J. Levesque, “Teamwork,” Noûs, No. 25, 487 (1991). https://doi.org/10.2307/2216075

  10. S. Datta, K. Bhaduri, C. Giannella, R. Wolff, and H. Kargupta, “Distributed data mining in peer-to-peer networks,” IEEE Internet Comput. 10 (4), 18–26 (2006). https://doi.org/10.1109/mic.2006.74

    Article  Google Scholar 

  11. G. Di Marzo Serugendo, M. P. Gleizes, and A. Karageorgos, “Self-organisation in multi-agent systems,” in Rapport de recherche IRIT/2005_18_R (Univ. Paul Sabatier, Toulouse, 2005).

    Google Scholar 

  12. J. Ferber, “Foreword,” in Handbook on Agent-Oriented Design Processes, Ed. by M. Cossentino, V. Hilaire, A. Molesini, and V. Seidita (Springer, Berlin, 2014). https://doi.org/10.1007/978-3-642-39975-6

    Book  Google Scholar 

  13. FIPA P2P NA WG6. Functional Architecture Specification Draft 0.12. http://www.fipa.org/subgroups/P2PNA-WG-docs/P2PNA-Spec-Draft0.12.doc.

  14. K. Geihs, “Engineering challenges ahead for robot teamwork in dynamic environments,” Appl. Sci. 10, 1368 (2020). https://doi.org/10.3390/app10041368

    Article  Google Scholar 

  15. I. Gidaspov and A. Akopyan, “What is DeFi: Everything about decentralized finance.” https://currency.com/ru/chto-takoe-defi

  16. V. Gorodetsky, “Multi-agent autonomous group control in collective robotics-based assembly,” CEUR Workshop Proc. 2648, 1 (2020).

    Google Scholar 

  17. V. I. Gorodetskii, “Self-organization and multiagent systems: I. Models of multiagent self-organization,” J. Comput. Syst. Sci. Int. 51, 256–281 (2012). https://doi.org/10.1134/s106423071201008x

    Article  MathSciNet  MATH  Google Scholar 

  18. V. I. Gorodetskii, “Self-organization and multiagent systems: II. Applications and the development technology,” J. Comput. Syst. Sci. Int. 51, 391–409 (2012). https://doi.org/10.1134/s1064230712020062

    Article  MATH  Google Scholar 

  19. V. I. Gorodetskii, O. V. Karsaev, V. V. Samoilov, and D. Trotskii, “Development tools for open agent networks,” J. Comput. Syst. Sci. Int. 47, 429–446 (2008). https://doi.org/10.1134/S1064230708030131

    Article  MATH  Google Scholar 

  20. V. Gorodetsky, O. Karsaev, V. Samoylov, and S. Serebryakov, “P2P agent platform: Implementation and testing,” in Agents and Peer-to-Peer Computing, Ed. by S. R. H. Joseph, Z. Despotovic, G. Moro, and S. Bergamaschi, Lecture Notes in Computer Science, Vol. 5319 (Springer, Berlin, 2010), pp. 41–54. https://doi.org/10.1007/978-3-642-11368-0_4

  21. B. J. Grosz and S. Kraus, “Collaborative plans for complex group action,” Artif. Intell. 86, 269–357 (1996). https://doi.org/10.1016/0004-3702(95)00103-4

    Article  MathSciNet  MATH  Google Scholar 

  22. N. R. Jennings, “Controlling cooperative problem solving in industrial multi-agent systems using joint intentions,” Artif. Intell. 75, 195–240 (1995). https://doi.org/10.1016/0004-3702(94)00020-2

    Article  Google Scholar 

  23. G. A. Kaminka and I. Frenkel, “Towards flexible teamwork in behavior-based robots,” in Proc. 4th Int. Joint Conf. on Autonomous Agents and Multiagent Systems (Association for Computing Machinery, New York, 2005), pp. 1355–1356. https://doi.org/10.1145/1082473.1082770

  24. A. Karelina, “What is Web3: Explanation in simple words.” https://secretmag.ru/enciklopediya/chto-takoe-web3-obyasnyaem-prostymi-slovami.htm

  25. L. Lee, T. Braud, P. Zhou, L. Wang, D. Xu, Z. Lin, A. Kumar, C. Bermejo, and P. Hui ar**v:2110.05352 (2021).

  26. M. May and L. Saitta, Ubiquitous Knowledge Discovery: Challenges, Techniques, Applications, Lecture Notes in Computer Science, Vol. 6202 (Springer, Berlin, 2010). https://doi.org/10.1007/978-3-642-16392-0

    Book  Google Scholar 

  27. Ch. Moemeng, V. Gorodetsky, Z. Zuo, Yo. Yang, and Ch. Zhang, “Agent-based distributed data mining: A survey,” in Data Mining and Multi-agent Integration, Ed. by L. Cao (Springer, Boston, 2009), pp. 47–58. https://doi.org/10.1007/978-1-4419-0522-2_3

    Book  Google Scholar 

  28. A. L. Prodromidis, Ph. K. Chan, and S. J. Stolfo, “Meta-learning in distributed data mining systems: Issues and approaches,” in Advances of Distributed Data Mining (AAAI Press, 2000).

    Google Scholar 

  29. K. Sycara, M. Paolucci, M. Van Velsen, and J. Giampapa, “The RETSINA multi-agent infrastructure,” Auton. Agents Multi-Agent Syst. 7, 29–48 (2003). https://doi.org/10.1023/a:1024172719965

    Article  Google Scholar 

  30. M. Tambe, W.-M. Shen, M. Mataric, D. Pynadath, D. Goldberg, P. Modi, Z. Qiu, and B. Salemi, “Teamwork in cyberspace: Using TEAMCORE to make agents team-ready,” in AAAI Spring Symp. on Agents in Cyberspace (1999), pp. 136–141.

  31. M. Tambe, “Towards flexible teamwork,” J. Artif. Intell. Res. 7, 83–124 (1997). https://doi.org/10.1613/jair.433

    Article  Google Scholar 

  32. R. Wolff and A. Schuster, “Association rule mining in peer-to-peer systems,” IEEE Trans. Syst., Man Cybern., Part B (Cybern.) 34, 2426–2438 (2004). https://doi.org/10.1109/tsmcb.2004.836888

    Article  Google Scholar 

  33. D. Ye, M. Zhang, and A. V. Vasilakos, “A survey of self-organization mechanisms in multiagent systems,” IEEE Trans. Syst., Man, Cybern.: Syst. 47, 441–461 (2017). https://doi.org/10.1109/tsmc.2015.2504350

    Article  Google Scholar 

  34. Ya. Zhang, “Mobile edge computing,” in Mobile Edge Computing, Simula SpringerBriefs on Computing, Vol. 9 (Springer, Cham, 2021), pp. 9–21. https://doi.org/10.1007/978-3-030-83944-4_2

Download references

Author information

Authors and Affiliations

  1. Joint Stock Company EUREKA, 196006, St. Petersburg, Russian Federation

    V. I. Gorodetsky

Authors
  1. V. I. Gorodetsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. I. Gorodetsky.

Ethics declarations

The author declares that he has no conflicts of interest.

Additional information

Vladimir Ivanovich Gorodetsky. Doctor of Technical Sciences (1973), Professor (1990), Honored Scientist of the Russian Federation. He graduated from the Leningrad Air Force Engineering Academy with the master degree in mechanics (1960) and the Faculty of Mathematics and Mechanics of the Leningrad State University with the master degree in mathematics (1970). He defended his candidate (1967) and doctoral (1973) dissertations on the problems of optimal control. From 1967 to 1988 he worked at the Mozhaisky Military Space Engineering Academy in research and teaching positions. From 1988 to August 10, 2018, he worked at the St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences as head of the laboratory and chief researcher of the laboratory of intelligent systems. He published more than 250 works (over 100 in foreign cited publications) and 9 monographs and manuals; he was coeditor of 12 issues of the “Lecture Notes in Artificial Intelligence” and “Lecture Notes in Computer Science” series (2001–2015), guest editor of a special issue of the IEEE Intelligent Systems journal (Q1) on “Agent and Data Mining Interaction” (2009), etc. Member of the Russian and European Association of Artificial Intelligence, IEEE, IEEE Computer Science, International Foundation for Autonomous Agents and Multi-agent Systems (IFAAMAS), International Society of Information Fusion (ISIF). Associate Editor of the Data Science and Analytics international journal (Springer) and a member of the editorial board of the Design Ontology Russian journal. Winner of the D.A. Pospelov Award of the Russian Association of Artificial Intelligence (2022).

Science metric data (as of January 1, 2023): RSCI citation 3166, h-index = 24, WoS citation 219, h-index = 7, Scopus-citation 755, h-index =12.

Areas of scientific interest: theory of optimal control, celestial mechanics, applied statistics, planning and scheduling, artificial intelligence and decision-making, multi-agent systems, group management, self-organizing agent networks, software tools, data mining and machine learning, efficient and robust big data processing algorithms, semantic computing, distributed and p2p-learning, computer security, dealing with uncertainty, recommendation systems, transport and production logistics, steganography, etc.

Web page: https://en.wikipedia.org/wiki/Vladimir_Gorodetski.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gorodetsky, V. Basic Trends of Decentralized Artificial Intelligence. Pattern Recognit. Image Anal. 33, 324–333 (2023). https://doi.org/10.1134/S105466182303015X

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S105466182303015X

Keywords:

Search

Navigation