Interactive Situated Autonomic Multi-Agents System-Comprehensive Survey
Multi-Agent Systems (MAS) are made up of autonomous entities called agents. Agents collaborate to complete tasks, but their intrinsic capacity to learn and make independent judgments allows them to be more flexible. Agents learn new contexts and behaviors through their interactions with other agents as well as the environment. Agents then exploit their knowledge to determine and carry out an action on the environment in order to accomplish their assigned objective. Because of its versatility, MAS is well suited to solving issues in a wide range of areas, including computer science, civil engineering, electrical engineering, etc. Developing cooperative MAS necessitates tackling a variety of issues, especially coordination among agents. Consequently, this paper discusses several interaction ways among agents in many disciplines. Index Terms—multi-agent system(MAS), Robotic Process Automation, situated agents, interaction.
 M. Luck, P. McBurney, O. Shehory, and S. Willmott, Agent technology, Computing as Interaction: A Roadmap for Agent Based Computing. University of Southampton on behalf of AgentLink III, 2005.
 M. Luck, P. McBurney, and C. Preist, Agent technology: enabling next generation computing (a roadmap for agent based computing). AgentLink, 2003.
 M. Wooldridge, “Reaching agreements,” an Introduction to Multi-agent Systems, John Wiley & Sons, Ltd, 2002.
 N. R. Jennings and S. Bussmann, “Agent-based control systems: Why are they suited to engineering complex systems?” IEEE control systems magazine, vol. 23, no. 3, pp. 61–73, 2003.
 M. Wooldridge, An introduction to multiagent systems. John wiley & sons, 2009.
 R. Kamdar, P. Paliwal, and Y. Kumar, “A state of art review on various aspects of multi-agent system,” Journal of Circuits, Systems and Computers, vol. 27, no. 11, p. 1830006, 2018.
 M. Wooldridge and N. R. Jennings, “Intelligent agents: Theory and practice, 1995,” Knowl. Eng. Rev, vol. 10, pp. 2–115, 2006.
 W. Van der Aalst and K. Van Hee, “Workflow management: models, methods, and systems,” 2002.
 S. Chandler, C. Power, M. Fulton, and N. Van Nueten, “Who minds the bots,” Why Organisations Need to Consider Risks Related to Robotic Process Automation, 2017.
 C. Le Clair, A. Cullen, and M. King, “The forrester wave™: Robotic process automation, q1 2017,” Forrester Research, 2017.
 W. M. Van der Aalst, M. Bichler, and A. Heinzl, “Robotic process automation,” pp. 269–272, 2018.
 C. Tornbohm and R. Dunie, “Gartner market guide for robotic process automation software,” Report G00319864. Gartner, 2017.
 S. Ibarra-Mart´ınez, J. Castan-Rocha, J. Laria-Menchaca, J. Guzm ´ an- ´ Obando, and E. Castan-Rocha, “Reaching high interactive levels with ´ situated agents,” Ingenier´ıa, investigacion y tecnolog ´ ´ıa, vol. 14, no. 1, pp. 37–42, 2013.
 M. Bratman, “Intention, plans, and practical reason,” 1987.
 M. P. Georgeff and A. L. Lansky, “Reactive reasoning and planning.” in AAAI, vol. 87, 1987, pp. 677–682.
 A. Obied et al., “Intelligent software agent in e-health,” Journal of AlQadisiyah for computer science and mathematics, vol. 13, no. 1, pp. Page–99, 2021.
 A. Obied, “Deliberative regulation with self-organising sensing,” Adv. Comput, vol. 7, no. 3, pp. 80–94, 2017.
 A. Obied, M. A. Hajer, and A. H. Hasan, “Computerized situated agent as mediator in centralized computing market,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 2, pp. 344– 350, 2019.
 A. H. Jabber and A. Obied, “A multi-agent system in e-health system implementing EBDI model,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 14, pp. 2845– 2859, 2021.
 A. H. Jabber and A. Obied, “Implementing the EBDI model in an e-health system,” International Journal of Nonlinear Analysis and Applications, vol. 13, no. 1, pp. 1827–1839, 2022.
 J. L. d. l. Rosa, B. Innocenti, M. Montaner, A. Figueras, I. Munoz, and J. Ramon, “Team descriptions-small-size robot (f180) league-rogi team description,” Lecture Notes in Computer Science, vol. 2377, pp. 587–590, 2002.
 C. Quintero and J. Ll, “de la rosa, j. veh´ı, physical intelligent agents’ capabilities management for sure commitments in a collaborative world, frontier in artificial intelligence and applications,” IOS Press, ISBN I, vol. 58603, no. 466, p. 9, 2004.
 A. M. Kareem and A. Obied, “Testbed for intelligent agent: A survey,” Journal of Al-Qadisiyah for computer science and mathematics, vol. 13, no. 2, pp. Page–23, 2021.
 F. L. Da Silva, G. Warnell, A. H. R. Costa, and P. Stone, “Agents teaching agents: a survey on inter-agent transfer learning,” Autonomous Agents and Multi-Agent Systems, vol. 34, no. 1, 2019.
 F. L. Da Silva, M. E. Taylor, and A. H. R. Costa, “Autonomously reusing knowledge in multiagent reinforcement learning.” in IJCAI, 2018, pp. 5487–5493.
 A. M. Kareem and A. Obied, “Gridworld testbed for intelligent agent architecture,” Design Engineering, pp. 10 372–10 391, 2021.
 G. Santos, T. Pinto, I. Pracca, and Z. Vale, “Mascem: Optimizing the performance of a multi-agent system,” Energy, vol. 111, pp. 513–524, 2016.
 P. Faria, J. Spinola, and Z. Vale, “Aggregation and remuneration of electricity consumers and producers for the definition of demand-response programs,” IEEE Transactions on Industrial Informatics, vol. 12, no. 3, pp. 952–961, 2016.
 A. S. Gazafroudi, T. Pinto, F. Prieto-Castrillo, J. Prieto, J. M. Corchado, A. Jozi, Z. Vale, and G. K. Venayagamoorthy, “Organization-based multi-agent structure of the smart home electricity system,” in 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 1327–1334.
 A. S. Gazafroudi, F. Prieto-Castrillo, T. Pinto, A. Jozi, and Z. Vale, “Economic evaluation of predictive dispatch model in mas-based smart home,” in International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer, 2017, pp. 81–91.
 A. S. Gazafroudi, T. Pinto, F. Prieto-Castrillo, J. M. Corchado, O. Abrishambaf, A. Jozi, and Z. Vale, “Energy flexibility assessment of a multi agent-based smart home energy system,” in 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB). IEEE, 2017, pp. 1–7.
 C. G. Quintero Monroy et al., Introspection on control-grounded capabilities. An agent inspired approach for control. Universitat de Girona, 2007.
 L. Gomes, P. Faria, H. Morais, Z. Vale, and C. Ramos, “Distributed, agent-based intelligent system for demand-response program simulation in smart grids,” IEEE Intelligent Systems, vol. 29, pp. 56–65, 2014.
 S. Woltmann, A. Coordes, M. Stomberg, and J. Kittel, “Using multiagent systems for demand-response aggregators: a technical implementation,” in 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1. IEEE, 2020, pp. 911–918.
 C. Patsonakis, A. D. Bintoudi, K. Kostopoulos, I. Koskinas, A. C. Tsolakis, D. Ioannidis, and D. Tzovaras, “Optimal, dynamic and reliable demand-response via openadr-compliant multi-agent virtual nodes: Design, implementation & evaluation,” Journal of Cleaner Production, vol. 314, p. 127844, 2021.
 Z. Vale, T. Pinto, I. Praca, and H. Morais, “Mascem: electricity markets simulation with strategic agents,” IEEE Intelligent Systems, vol. 26, no. 2, pp. 9–17, 2011.
 P. Oliveira, T. Pinto, H. Morais, and Z. Vale, “Masgrip—a multi-agent smart grid simulation platform,” in 2012 IEEE Power and Energy Society General Meeting. IEEE, 2012, pp. 1–8
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