Abstract
This paper illustrates a conceptual framework for the development of Monitoring and Control Systems (MCS), based on a four level agent-based architecture. Traditional MCS are designed according to a three–level architectural pattern, in which intelligent devices are usually devoted to evaluate if data acquired by a set of sensors could be interpreted as anomalous or not.
Possible mistakes in the evaluation process, due to faulty sensors or external factors, can cause the generation of undesirable false alarms. To solve this problem, our framework introduces a fourth level to the traditional MCS architecture, named correlation level, where an intelligent module, usually a Knowledge–Based System, collects the local interpretations made by each evaluation device building a global view of the monitored field. In this way, possible local mistakes are identified by the comparison with other local interpretations. The framework has been adopted for the development of Automotive MCSs.
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References
Westra, R.L.: Design of a knowledge-based monitoring and control system, Technical report, Department PRG/Faculty Math. and Comp. Sc., University of Amsterdam, Esprit 2439 report D31, N.J. (1989)
Wilson, A., Hendler, J.: Linking Symbolic and Subsymbolic Computing, Technical Report, Dept. of Computer Science, University of Maryland (1993)
Ferrier, N.J., Rowe, S.M., Blake, A.: Real–time traffic monitoring. In: WACV 1994, pp. 81–88 (1994)
Genesereth, M.R.: Software agents. Communication of the ACM 37(7), 48–53 (1994)
Honavar, V.: Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Toward a Resolution of the Dichotomy. In: Sun, R., Bookman, L. (eds.) Computational Architectures Integrating Symbolic and Neural Processes, pp. 351–388. Kluwer, New York (1994) (Invited chapter)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)
Woolridge, M., Jennings, N.R.: Intelligent agents: Theory and practice. Knowledge Engineering Review 10(2), 115–152 (1995)
Sobajic, D.: Applications of neural networks in environment, energy and health. In: Progress in Neural Processing 5, ch. 11. World Scientific Publishing Co. Ptd. Ltd., Singapore (1996)
Ferber, J.: Multi-Agent System: An Introduction to Distributed Artificial Intelligence. Addison Wesley Longman, Harlow (1999)
Georgeff, M., Pell, B., Pollack, M., Tambe, M., Wooldridge, M.: The Belief-Desire-Intention Model of Agency. In: Rao, A.S., Singh, M.P., Müller, J.P. (eds.) ATAL 1998. LNCS, vol. 1555, pp. 1–10. Springer, Heidelberg (1999)
Brossette, S., et al.: A data mining system for infection control surveillance, Technical report, Department of Pathology, University of Alabama at Birmingham, USA (2000)
Craciun, F., Kope, Z., Letia, I.A., Netin, A.: Distributed Diagnosis By Bdi Agents. In: Proceedings of the IASTED International Conference Applied Informatics, Innsbruck, Austria, February 14-17 (2000)
Jennings, N.R.: On agent-based software egineering. Artificial Intelligence 117, 277–296 (2000)
Lyon, D.: Surveillance Society: Monitoring Everyday Life. Open University Press, Stony Stratford (2001)
Prasad, V.K.: What Pervasive Computing Brings to Automotive Consumer Experiences. In: Presented at PC2001 NIST, Gaithersberg, Maryland (2001), available at: http://www.nist.gov/pc2001/agenda.html
Bandini, S., et al.: Knowledge-Based Alarm Correlation in Traffic Monitoring and Control. In: Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems (ITSC 2002), Singapore (2002)
Egmont, M., et al.: Image processing with neural networks: a review. Pattern Recognition 35(10), 2279–2301 (2002)
Heffernan, D., Leen, G.: Expanding Automotive Electronic Systems. IEEE Computer 35(1), 48–53 (2002)
Herrtwich, R.G.: Ubiquitous Computing in the Automotive Domain. In: Mattern, F., Naghshineh, M. (eds.) PERVASIVE 2002. LNCS, vol. 2414, p. 15. Springer, Heidelberg (2002)
Bandini, S., et al.: Intelligent Alarm Correlation. In: Proceedings of 2003 IEEE International Conference on Systems, Man and Cybernetics (Invited Session on “Modelling and control of transportation and traffic systems”), Washington DC (2003)
Palmonari, M., Sartori, F.: Towards the Improvement of Monitoring and Control Agencies through Knowledge-Based Approaches. In: Proceedings of WOA 2003, Villasimius, Italy (2003)
Satyanarayanan, M.: Privacy: the achilles heel of pervasive computing? IEEE Pervasive Computing-Mobile and Ubiquitous Systems (2003), Available at: http://www.computer.org/pervasive/pc2003/b1002.pdf
Jo, C.-H., Chen, G., Choi, J.: A new approach to the BDI agent-based modeling. In: Proceedings of the 2004 ACM Symposium on Applied Computing (SAC), Nicosia, Cyprus (2004)
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Bandini, S., Mosca, A., Palmonari, M., Sartori, F. (2004). A Conceptual Framework for Monitoring and Control System Development. In: Baresi, L., Dustdar, S., Gall, H.C., Matera, M. (eds) Ubiquitous Mobile Information and Collaboration Systems. UMICS 2004. Lecture Notes in Computer Science, vol 3272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30188-2_9
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DOI: https://doi.org/10.1007/978-3-540-30188-2_9
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