Open Access Peer-reviewed Commentary

Ensembles of intelligent agents with expanding communication abilities

Article Sidebar

General approach to the development of intellectual agent ensembles
Download PDF
Submitted   Nov 6, 2022
Published   Dec 12, 2022
Issue    Vol 1 No 1 (2022)
DOI   10.25082/RIMA.2022.01.005

Views

600

Downloads

351

Citations

PlumX Metrics

Main Article Content

Evgeniy Bryndin corresponding author
Research Centre "NATURAL INFORMATIC", National Supercomputer Technological Platform, Novosibirsk, Russia

Abstract

Ensembles of intellectual agents solve the problem in the course of self-organization and cooperation according to the criteria of preference and restriction. The solution is considered found when, in the course of their nondeterministic interactions, agents reach the best consensus (temporary equilibrium or balance of interests), which is taken as a solution to the problem. Solving a problem is always seen as an equilibrium when none of the agents can improve their condition anymore, which is evidence of reaching a reasonable compromise, balance of interests, or agreement (harmony) of all intellectual agents in a problematic situation. Agents can act both on behalf of and on behalf of a person, and any physical and abstract entities. In the ensemble of intellectual agents of each entity of the real world, a software agent is put in line, which represents the interests of this entity and can coordinate its decisions with other agents. The advantages of intelligent agents that allow you to build self-organizing ensembles are especially manifested in conditions of a priori uncertainty and high dynamics of the world around you, allowing you to build adaptive ensembles with communicative abilities, rebuilding your plans for events in real time. The higher the intelligence of each agent and the richer the opportunities for communication between agents, the more complex and creative behavior the ensemble can demonstrate. The intellect of the ensemble arises and manifests itself in the process of self-organization of intellectual agents.

Keywords
ensembles of intellectual agents, self-organization of adaptive ensembles, preference criterion, communicative abilities

Article Details

How to Cite
Bryndin, E. (2022). Ensembles of intelligent agents with expanding communication abilities. Research on Intelligent Manufacturing and Assembly, 1(1), 35-40. https://doi.org/10.25082/RIMA.2022.01.005
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. Bigus JP. The Agent Building and Learning Environment. Proceedings of Autonomous Agents 2000, Barcelona, ACM Press (2000), pp. 108-109. https://doi.org/10.1145/336595.337066
  2. Olfati-Saber R and Murray RM. Consensus problems in networks of agents with switching topology and time-delays. Automatic Control, IEEE Transactions, 2004, 49(9): 1520-1533. https://doi.org/10.1109/TAC.2004.834113
  3. North MJ, Howe TR, Collier NT, et al. Repast Simphony Development Environment, Proceedings of the Agent 2005 Conference on Generative Social Processes, Models, and Mechanisms, ANL/DIS-06- 1, co-sponsored by Argonne National Laboratory and The University of Chicago, 20005, 13-15.
  4. Zambonelli F, Jennings NR and Wooldridge M. Multi-agent systems as computational organizations: the Gaia methodology. Agent-oriented methodologies. IGI Global, 2005: 136-171. https://doi.org/10.4018/978-1-59140-581-8.ch006
  5. North MJ, Howe TR, Collier NT, et al. Containing agents: Context, Projections and Agents. Proceedings of the Agent 2006 Conference on Social Agents: Results and Prospects, ANL/DIS-06-7, co-sponsored by Argonne National Laboratory and The University of Chicago, September 21-23.
  6. Olfati-Saber R, Fax JA and Murray RM. Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 2007, 95(1): 215-233. https://doi.org/10.1109/JPROC.2006.887293
  7. Sun Y and Jiong R. Leader–follower consensus problems of multi-agent systems with noise perturbation and time delays. Chinese Physics Letters, 2008, 25(9): 3493. https://doi.org/10.1088/0256-307X/25/9/107
  8. Wooldridge M. An Introduction to MultiAgent Systems.Wooldridge 2nd edition. Chichester, England: John Wiley & Sons, 2009.
  9. Aaron E and Admoni H. A framework for dynamical intention in hybrid navigating agents. International Conference on Hybrid Artificial Intelligence Systems. Springer, Berlin, Heidelberg, 2009: 18-25. https://doi.org/10.1007/978-3-642-02319-4_3
  10. Chebotarev PY and Agaev RP. Coordination in multiagent systems and Laplacian spectra of digraphs. Automation and Remote Control, 2009, 70(3): 469-483. https://doi.org/10.1134/S0005117909030126
  11. Aaron E and Admoni H. Action selection and task sequence learning for hybrid dynamical cognitive agents. Robotics and Autonomous Systems, 2010, 58(9): 1049-1056. https://doi.org/10.1016/j.robot.2010.05.006
  12. Aaron E and Mendoza JP. Dynamic obstacle representations for robot and virtual agent navigation. Canadian Conference on Artificial Intelligence. Springer, Berlin, Heidelberg, 2011: 1-12. https://doi.org/10.1007/978-3-642-21043-3_1
  13. Aaron E, Mendoza J P, Admoni H. Integrated Dynamical Intelligence for Interactive Embodied Agents. ICAART (2). 2011: 296-301.
  14. Chen Y, Lu J, Yu X, et al. Multi-agent systems with dynamical topologies: Consensus and applications. IEEE circuits and systems magazine, 2013, 13(3): 21-34. https://doi.org/10.1109/MCAS.2013.2271443
  15. Lewis FL, Zhang H, Hengster-Movric K, et al. Cooperative control of multi-agent systems: optimal and adaptive design approaches. Springer Science & Business Media, 2013. https://doi.org/10.1007/978-1-4471-5574-4
  16. Camara M, Bonham-Carter O and Jumadinova J. A multi-agent system with reinforcement learning agents for biomedical text mining. Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, 2015: 634-643. https://doi.org/10.1145/2808719.2812596
  17. Deryugin D. Applying multi-agent technologies to control a group of robotic devices. Fundamental Informatics and Information Technology Mathematical and Software Support for Computers, Computer Systems and Networks. Saint Petersburg State University, 2017: 1-30.
  18. Lombardo G, Fornacciari P, Mordonini M, et al. A Multi-Agent Architecture for Data Analysis. Future Internet, 2019, 11(2): 49. https://doi.org/10.3390/fi11020049
  19. Koshur VD. Implementation of a multi-agent artificial intelligence system to solve the classification problem/V. D. Koshur, V. I. Rozhkov. Neuroinformatics-2019: materials of an international scientific and technical conference. Moscow: MIPT Publishing House, 2019, Part 1: 24-31.
  20. Yu L, Wang S and Lai K K. A multi-agent neural network system for web text mining
  21. [M]//Emerging technologies of text mining: techniques and applications. IGI Global, 2008: 162-183.
  22. Burlutsky VV, Keramov ND, Baluev VA, et al. Development of a multi-agent intellectual system for solving the problems of classification and ranking of materials on the Internet. Bulletin of the South, 20200, 16(3): 47-52. https://doi.org/10.17816/byusu2020347-52
  23. Bryndin E. Cross-Platform Collaboration with Help Virtual Digital Thinking of Technological Mind with Artificial Intelligence. Integrative Journal of Conference Proceedings, 2022, 3(1): 1-4.
  24. Bryndin E. Collaboration of intelligent interoperable agents via smart interface. International Journal on Data Science and Technology, 2019, 5(4): 66-72. https://doi.org/10.11648/j.ijdst.20190504.11
  25. Bryndin E. Development of artificial intelligence by ensembles of virtual agents with mobile interaction. Automation, Control and Intelligent Systems, 2020, 8(1): 1-8. https://doi.org/10.11648/j.acis.20200801.11
  26. Bryndin E. Technology Self-organizing Ensembles of Intelligent Agents with Collective Synergetic Interaction. Automation, Control and Intelligent Systems, 2020, 8: 29-37. https://doi.org/10.11648/j.acis.20200804.11
  27. Bryndin E. Functional and Harmonious Self-Organization of Large Intellectual Agent Ensembles with Smart Hybrid Competencies. American Journal of Software Engineering and Applications, 2021, 10(1): 1-10. https://doi.org/10.11648/j.ajsea.20211001.11
  28. Bryndin E. Simulation of Creative Manifestation by Functional Ensembles of Intellectual Agents Based on Live Information in Various Spheres of Life Activity. Network and Communication Technologies, 2021, 6(2): 1-41. https://doi.org/10.5539/nct.v6n2p41
  29. Bryndin E. Modeling of creative and professional activities by ensembles of intellectual agents based on live information. International Journal of Artificial Intelligence and Mechatronics (IJAIM), 2022, 10(4): 44-50.
  30. Bryndin E. Ensembles of Intellectual Agents with Decision-Making. Acta Scientific Computer Sciences, 2022, 4(6): 3-8. https://doi.org/10.33258/bioex.v4i3.752
  31. Bryndin E. Intellectual Agent Ensemble with Professional Competencies, Pattern Recognition and Decision Making. Applied Science and Innovative Research, 2022, 6(4): 1-10. https://doi.org/10.22158/asir.v6n4p1
  32. Bryndin E. Identification of Natural Novelty and Disasters by Ensembles of Intelligent Agents Based on Spectral Measurement. International Journal of Innovative Research in Multidisciplinary Education, 2022, 1(2): 55-59.
  33. Bryndin E. Formation and Management of Industry 5.0 by Systems with Artificial Intelligence and Technological Singularity. American Journal of Mechanical and Industrial Engineering, 2020, 5(2): 24-30.
  34. Bryndin E. Implementation of Competencies by Smart Ethical Artificial Intelligence in Different Environments. Software Engineering, 2021, 8(4): 24-33 https://doi.org/10.11648/j.se.20200804.11
  35. Bryndin E. Creating humanoid intelligent digital twin on spectral and holographic approaches. Research on Intelligent Manufacturing and Assembly, 20022, 1(1): 28-34. https://doi.org/10.25082/RIMA.2022.01.004