Abstract
A multi-agent based prediction-system is presented in which the aim is to forecast the presence of oil slicks in a certain area of the open sea after an oil spill. In this case, the multi-agent architecture incorporates a prediction-system based on the CBR methodology, implemented in a series of interactive services, for modeling and monitoring the ocean water masses. The system’s nucleus is formed by a series of deliberative agents acting as controllers and administrators for all the implemented services. The implemented services are accessible in a distributed way, and can be accessed even from mobile devices. The proposed system uses information such as sea salinity, sea temperature, wind, currents, pressure, number and area of the slicks, etc. obtained from various satellites. The system has been trained using data obtained after the Prestige accident. The Oil Spill Multi-Agent System (OSM) has been able to accurately predict the presence of oil slicks in the north-west of the Galician coast using historical data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aamodt, A. and Plaza, E., Case-Based Reasoning: foundational Issues, Methodological Variations, and System Approaches, AI Communications, 7 (1) (1994), 39–59.
Bratman, M.E., Intentions, plans and practical reason, Harvard University Press, Cambridge, MA, USA. (1987)
Bratman, M.E., Israel, D. and Pollack, M.E., Plans and resource-bounded practical reasoning, Computational Intelligence, 4 (1988), 349–35.
Brovchenko, I., Kuschan, A., Maderich, V. and Zheleznyak, M., The modelling system for simulation of the oil spills in the Black Sea, 3rd EuroGOOS Conference: Building the European capacity in operational oceanography., 192 (2002).
Cerami, E., Web Services Essentials Distributed Applications with XML-RPC, SOAP, UDDI & WSDL, O’Reilly & Associates, Inc.
Corchado, J.M., Bajo, J., De Paz, Y. and Tapia, D.I., Intelligent Environment for Monitoring Alzheimer Patients, Agent Technology for Health Care, Decision Support Systems, In Press.
Corchado, J.M. and Fdez-Riverola, F., FSfRT: Forecasting System for Red Tides, Applied Intelligence, 21 (2004), 251–264.
Chen, Y.M. and Wang, S.C., Framework of agent-based intelligence system with twostage decision-making process for distributed dynamic scheduling,Applied Soft Computing Journal, 7 (1) (2007), 229–245.
Dunteman, G.H., Principal Components Analysis. Newbury Park, California (1989).
Fritzke, B., Growing cell structures-a self-organizing network for unsupervised and supervised learning, Neural Networks, 7 (9) (1994), 1441–1460.
Georgeff, M. and Rao, A., Rational software agents: from theory to practice, In Jennings, N.R.Y. Wooldridge, M.J. (eds), Agent Technology: Foundations, Applications, and Markets. Springer-Verlag New York, Secaucus, NJ, (1998), 139–160.
Gunter, S., Schraudolph, N.N. and Vishwanathan, S.V.N., Fast Iterative Kernel Principal Component Analysis, Journal of Machine Learning Research, 8 (2007), 1893–1918.
Haykin, S., Neural networks. Prentice Hall Upper Saddle River, NJ (1999).
Jayaputera, G.T., Zaslavsky, A.B. and Loke, S.W., Enabling run-time composition and support for heterogeneous pervasive multi-agent systems. Journal of Systems and Software, 80(12) (2007), 2039–2062.
Jennings, N.R. and Wooldridge, M., Applying agent technology. Applied Artificial Intelligence, 9(4) (1995), 351–361.
Karayiannis, N.B. and Mi, G.W., Growing radial basis neural networks: merging supervised andunsupervised learning with network growth techniques, Neural Networks, IEEE Transactions on, 8 (6) (1997), 1492–1506.
Menemenlis, D., Hill, C, Adcroft, A., Campin, J.M., et al., NASA Supercomputer Improves Prospects for Ocean Climate Research, EOS Transactions, 86 (9) (2005), 89–95.
Palenzuela, J.M.T., Vilas, L.G. and Cuadrado, M.S., Use of ASAR images to study the evolution of the Prestige oil spill off the Galician coast, International Journal of Remote Sensing, 27 (2006) (10), 1931–1950.
Plaza, E., Armengol, E. and Ontanon, S., The Explanatory Power of Symbolic Similarity in Case-Based Reasoning, Artificial Intelligence Review, 24 (2) (2005), 145–161.
Pokahr, A., Braubach, L. and Laniersdorf, W., Jadex: Implementing a BDI-Infrastructure for JADE Agents, In EXP — in search of innovation (Special Issue on JADE) (2003), 76–85.
Preisendorfer, R.W., Principal Component Analysis in Meteorology and Oceanography, Development in atmospheric science (1988).
Rao, A.S. and Georgeff, M.P., BDI agents: from, theory to practice, In Proceedings of the First International Conference on Multi-Agents Systems (ICMAS’95). San Francisco, CA, USA. (1995).
Ros, F., Pintore, M. and Chrétien, J.R., Automatic design of growing radial basis function neural networks based on neighbourhood concepts, Chemometrics and Intelligent Laboratory Systems, 87 (2) (2007), 231–240.
Ross, B.J., Gualtieri, A.G., Fueten, F., Budkewitsch, P., et al., Hyperspectral image analysis using genetic programming, Applied Soft Computing, 5(2) (2005), 147–156.
Schn, B., O’Hare, G.M.P., Duffy, B.R., Martin, A.N., et al., Agent Assistance for 3D World Navigation, Lecture Notes in Computer Science, 3661(2005), 499–499.
Snidaro, L. and Foresti, G.L., Knowledge representation for ambient security, Expert Systems, 24(5) (2007), 321–333.
Solberg, A.H.S., Storvik, G., Solberg, R. and Volden, E., Automatic detection of oil spills in ERS SAR images, IEEE Transactions on Geoscience and Remote Sensing, 37 (4) (1999), 1916–1924.
Vethamony, P., Sudheesh, K., Babu, M.T., Jayakumar, S., et al., Trajectory of an oil spill off Goa, eastern Arabian Sea: Field observations and simulations, Environmental Pollution (2007).
Wooldridge, M. and Jennings, N.R., Intelligent Agents: Theory and Practice, The Knowledge Engineering Review, 10(2) (1995), 115–152.
Yang, J. and Luo, Z., Coalition formation mechanism in multi-agent systems based on genetic algorithms, Applied Soft Computing Journal, 7(2) (2007), 561–568.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Birkhäuser Verlag Basel/Switzerland
About this chapter
Cite this chapter
Corchado, J.M., Mata, A., Rodriguez, S. (2009). OSM: A Multi-Agent System for Modeling and Monitoring the Evolution of Oil Slicks in Open Oceans. In: Cortés, U., Poch, M. (eds) Advanced Agent-Based Environmental Management Systems. Whitestein Series in Software Agent Technologies and Autonomic Computing. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8900-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-7643-8900-0_5
Publisher Name: Birkhäuser Basel
Print ISBN: 978-3-7643-8897-3
Online ISBN: 978-3-7643-8900-0
eBook Packages: Computer ScienceComputer Science (R0)