BEHAVIORAL MODEL FOR CYBER-PHYSICAL SYSTEM AND GROUP CONTROL: THE BASIC CONCEPTS
Abstract
The paper subject is a control problem in complex distributed cyber-physical systems pos-sessing heterogeneous resources of shared access. As a rule, such systems comprise formidable number of relatively simple autonomous and often mobile physical, virtual and social objects with embedded computational and communication capabilities. These objects are designed to jointly solve complex intelligent tasks through intensive interactions while exhibiting an intelligent behav-ior. The basic features of such systems are caused by the fact that they can solve concurrently several tasks and each of these tasks may involve a subset of the autonomous objects of the system. At that, each system object can participate, in parallel, in performance of several system tasks. As examples of applications of the system in question, one can mention complex robotic manufactur-ing, B2B-production and logistics networks, swarm robotics, swarm satellite-based distributed surveillance systems, and like called as group control systems. The paper proposes to revisit the theoretical foundation of modeling traditionally exploited for the considered class of systems that is knowledge-based paradigm of Artificial Intelligence and to use behavior-based paradigm in-stead of it. Accordingly, the paper objective is to introduce and describe the basic concepts of behavior-based ontology intended to specify scenario knowledge and data constituting the shared information space of distributed cyber-physical system. This behavior-based ontology should rep-resent domain-independent component of knowledge on group behavior and group control. In particular, the paper introduces such concepts of this ontology as group behavior scenario and its structure, event end event- driven control component, situation, exceptional situation, emergent situation, situational awareness, local, adaptive and terminal group control. The advances and novel features of the behavior-based paradigm focusing on scenario knowledge model compared to the knowledge-based paradigm of intelligent systems are demonstrated based on adaptive group control as applied to a manufacturing assembly system performed, in autonomous mode, by a group of robots.
References
2. Чень Ч., Ли Р. Математическая логика и автоматическое доказательство теорем. – М.: Наука, 1983. – 360 с.
3. Brooks R.A. Intelligence without Reasoning // Proc. of Intern. Joint Conf. on Artificial Intelli-gence (IJCAI'91). – 1991. – Vol. 1. – P. 569-595.
4. Steels L. Building Agents out of Autonomous Behavior Systems // The Artificial Life Route to Artificial Intelligence. Building Embodied Situated Agents / Eds L. Steels, and R.A. Brooks. Hillsdale, NJ: Lawrence Erlbaum Assoc., 1995. – P. 83-121.
5. Brooks R.A. A Robust Layered Control System for a Mobile Robot // IEEE J. of Robotics and Automation. RA-2. – 1986. – P. 14-23.
6. Городецкий В.И., Самойлов В.В., Троцкий Д.В. Базовая онтология коллективного пове-дения и ее расширения // Известия РАН Теория и системы управления. – 2015. – № 5. – C. 102-121.
7. Gershenson C. Behavior-based Knowledge Systems: An Epigenetic Path from Behavior to Knowledge // Proc. 2nd Workshop on Epigenetic Robotics. – Edinburgh, Scotland, 2002. – Vol. 94. – P. 35-41. – http://arxiv.org/ftp/cs/papers/0206/0206027.pdf.
8. Multi-agent Systems–A Modern Approach to Distributed Artificial Intelligence / Ed. G.Weiss. Boston: MIT Press, 1999. – 610 p.
9. Jennings N., Sycara K., Wooldridge M. A Roadmap of Agent Research and Development // Intern. J. Autonomous Agents and Multi-agent Systems. – Kluwer Academic Publishers. 1998. – No. 1. – P. 7-38.
10. Mataric M.J., Michaud F. Behavior-Based Systems // Handbook of Robotics. – Springer, 2008. – P. 891-909.
11. Городецкий В.И. Самоорганизация и многоагентные системы. I. Модели многоагентной самоорганизации // Известия РАН. ТИСУ. – 2012. – № 2. – C. 92-120.
12. Городецкий. В.И. Самоорганизация и многоагентные системы. II. Приложения и техно-логия разработки // Известия РАН. ТИСУ. – 2012. – № 3. – С. 102-123. 13. Allen J.F. Maintaining knowledge about temporal intervals // Communications of the ACM. – ACM Press, 1983: – P. 832-843. 14. Городецкий В.И., Дрожжин В.В., Юсупов Р.М. Многоуровневые атрибутные граммати-ки для моделирования сложных структурно-динамических систем // Известия АН СССР. Техническая кибернетика. – 1986. – № 1. – С. 165-172.
15. Lambert G.A. Grand Challenges of Information Fusion // Proc. Intern. Conf. Fusion-03. – Cairns, Australia, 2003. – P. 213–220.
16. Steinberg A.N., Bowman C.L., White F.E. Revisions to the JDL Data Fusion Model // A1AA Missile Sciences Conf. Naval Postgraduate School, CA, Monterey. 1998.
17. Fischer Y. On Situation Modeling and Recognition. Technical Report IES-2009-14 / Eds J. Beyerer, M. Huber. Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Insti-tute for Anthromatics, Vision and Fusion Laboratory. Karlsruhe, Germany. Kit Scientific Pub-lishing. 2009. – P. 203-215.
18. Endsley M.R. Towards a theory of Situation Awareness in Dynamic Systems // Human Fac-tors. – 1995. – Vol. 37. – P. 32-64. 19. Endsley M.R., Jones D.G. Designing for Situation Awareness: An Approach to User-Centered Design, Second Edition. – CRS Press Taylor & Francic Group, 2011. – 396 p.
20. Salmon P.M., Stanton N.A., Walker G.H., Jenkins D.P. Distributed Situation Awareness: Theo-ry, Measurement and Application to Teamwork. – CRS Press, 2017. – 266 p
21. Jennings N.R. Controlling Cooperative Problem Solving in Industrial Multi-Agent Systems Using Joint Intentions // In: Artificial Intelligence. – 1995. – Vol. 75. – P. 195-240.
22. Rzevski G., Skobelev P., Zhilyaev A., et al. Customization of Multi-Agent Systems for Adap-tive Resource Management with the Use of Domain Ontologies // International Journal of Economics and Statistics. – 2018. – Vol. 6. – P. 112-124.
