IJIS  Vol.4 No.4 , October 2014
Multi-Agent System for Real Time Planning Using Collaborative Agents
Abstract: Autonomous agents are an important area of research in the sense that they are proactive, and include: goal-directed and communication capabilities. Furthermore each goals of the agent are constantly changing in a dynamic environment. Part of the challenge is to automate the process corresponding to each agent in order that they find their own objectives. Agents do not have to work individually, but can work with others and develop a coordinated group of actions. These agents are highly appreciated, when real time problems are involved, meaning that an agent must be able to react within a specific time interval, considering external events. Our work focuses on the design of a multi-agent architecture consisting of autonomous agents capable of acting through a goal-directed with: a) constraints, b) real-time, and c) with incomplete knowledge of the environment. This paper shows a model of collaborative agents architecture that share a common knowledge source, allowing knowledge of the environment; where we analyze it and its changes, choosing the most promising way for achieving the goals of the agent, in order to keep the whole system working, even if a fault occurs.
Cite this paper: Laureano-Cruces, A. , Ramírez-González, T. , Sánchez-Guerrero, L. and Ramírez-Rodríguez, J. (2014) Multi-Agent System for Real Time Planning Using Collaborative Agents. International Journal of Intelligence Science, 4, 91-103. doi: 10.4236/ijis.2014.44011.

[1]   Laureano-Cruces, A.L., De Arriaga, F. and García-Alegre, M.C. (2001) Cognitive Task Analysis: A Proposal to Model Reactive Behaviors. Journal of Experimental & Theoretical Artificial Intelligence, 13, 227-239.

[2]   Quinones-Reyes, P., Benitez-Pérez, H., Mendez-Monroy, E., Cardenas-Flores, F. and García-Nocetti, F. (2006) Reconfigurable Network Control using Fuzzy Logic for Magnetic Levitation Case Study. IFAC SAFEPROCESS.

[3]   d’Inverno, M. and Luck, M. (2006) Understanding Agent Systems. 2nd Edition, Springer Verlag, Berlin.

[4]   Laureano-Cruces, A. and Barceló-Aspeitia, A. (2003) Formal Verification of Multi-Agent Systems Behavior Emerging from Cognitive Task Analysis. Journal of Experimental & Theoretical Artificial Intelligence, 15,407-431.

[5]   Brooks, R. (1991) Intelligence without Representation. Artificial Intelligence, 47, 139-159.

[6]   Benítez-Perez, H. and García-Nocetti, F. (2003) Reconfigurable Distributed Control Using Smart Peripheral Elements. Control Engineering Practice, 11, 975-988.

[7]   Frey, D., Nimis, J., Worn, H. and Lockermann, P. (2003) Benchmarking and Robust Multi-Agent-Based Production Planning and Control. Engineering Applications of artificial Intelligence, 16, 307-320.

[8]   Liu, J.W.S. (2000) Real Time Systems. Prentice Hall, Upper Saddle River.

[9]   Abdelzaher, T.F., Stankovic, J.A., Lu, C., Zhang, R. and Lu, Y. (2003) Feedback Performance Control in Software Services. Control Systems Magazine IEEE, 23, 74-90.

[10]   Bellahsene-Hatem, N.R., Mostefai, M. and El Kheir, A.O. (2011) Contribution au Diagnostic des évenéments défalliants dans un système Dinamique Hibride. Proceedings ICGST Conference on Computer Science and Engineering, Istanbul, 19-21 December 2011, 137-142.

[11]   Cervin, A., Henriksson, D., Lincoln, B., Eker, J. and Arzen, K. (2003) How Does Control Timing Affect Performance? Analysis and Simulation of Timing Using Jitterbug and True Time. IEEE Control Systems, 23, 16-30.

[12]   Ramírez-González, T., Quinones-Reyes, P., Benítez-Pérez, H. and Laureano-Cruces, A. (2006) Reconfigurable Fuzzy Takagi-Sugeno Networked Control Using Cooperative Agents and xPC Target. Proceedings of International Symposium on Robotics and Automation (ISRA 2006), San Miguel Regla, Hgo, Mexico, 25-28August 2006.

[13]   Dong-Chan, O. and Yong-Hwan, L. (2010) Cooperative Spectrum Sensing with Imperfect Feedback Channel in the Cognitive Radio Systems. International Journal of Communication Systems, 23, 763-779.

[14]   Laureano-Cruces, A. and De Arriga-Gómez, F. (1998) Multi-Agent Architecture for Intelligent Tutoring Systems. Journal of Interactive Learning Environments, Swets & Zeitlinger, 6, 225-250.

[15]   Laureano-Cruces, A., De Arriaga, F., Ramírez-Rodríguez, J. and Escarela-Pérez, R. (2006) Control of Intelligent Learning Systems with Reactive Characteristics. Interactive Learning Environments, Swets & Zeitlinger, 14.

[16]   Corkill, D. (2003) Collaborating Software: Blackboard and Multi-Agent Systems & the Future. Proceedings of the International Lisp Conference, New York.

[17]   Jennings, N.R. and Bussmann, S. (2003) Agent Based Control Systems: Why Are They Suited to Engineering Complex Systems? IEEE Control Systems, 23, 61-73.

[18]   Laureano-Cruces, A. and Espinosa-Paredes, G. (2005) Behavioral Design to Model a Reactive Decision of an Expert in Geothermal Wells. International Journal of Approximate Reasoning, 39, 1-28.

[19]   García-Zavala, A. (2003) Propuesta de un método de planificación para la reconfiguración en línea de un sistema de tiempo real. Master Degree Thesis, Posgrado en Ciencia e Ingeniería de la Computación, UNAM.

[20]   Almeida, L., Pedreiras, P. and Fonseca, J.A.G. (2002) The FTT-CAN Protocol: Why and How. IEEE Transactions on Industrial Electronics, 49, 1189-1201.

[21]   Low, S.H., Paganini, F. and Doyle, J.C. (2002) Internet Congestion Control. IEEE Control Systems, 22, 28-43.

[22]   Quinones-Reyes, P., Benitez-Pérez, H., Mendez-Monroy, E., Cardenas-Flores, F. and García-Nocetti, F. (2006) Reconfigurable Network Control Using Fuzzy Logic for Magnetic Levitation Case Study. IFAC SAFEPROCESS.

[23]   Tachwali, Y., Basma, F. and Refai, H. (2012) Adaptability and Configurability in Cognitive Radio Design on Small Form Factor Software Radio Platform. Wireless Personal Communications, 62, 1-29.