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Case-Based Reasoning on Images and Signals pp 213–246Cite as

A CBR Agent for Monitoring the Carbon Dioxide Exchange Rate from Satellite Images

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 73))

Summary

This work presents a multiagent system for evaluating automatically the interaction that exists between the atmosphere and the ocean surface, monitoring and evaluating within the ocean carbon dioxide exchange process is a function requiring working with a great amount of data: satellite images and in situ Vessel’s data. The system presented in this work focuses on Ambient Intelligence (AmI) technologies since the vision of AmI assumes seamless, unobtrusive, and often invisible but also controllable interactions between humans and technology. The work presents the construction of an open multiagent architecture which, based on the use of deliberative agents incorporating Case-Based Reasoning (CBR) systems, offers a distributed model for such an interaction. This work also presents an analysis and design methodology that facilitates the implementation of CBR agent-based distributed artificial intelligent systems. Moreover, the architecture takes into account the fact that the working environment is dynamic and therefore it requires autonomous models that evolve overtime. In order to resolve this problem an intelligent environment has been developed, based on the use of CBR agents, which are capable of handling several goals, constructing plans from the data obtained through satellite images and research Vessels, acquiring knowledge, and of adapting to environmental changes, are incorporated. The artificial intelligence system has been successfully tested in the North Atlantic ocean, and the results obtained will be presented within this work.

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References

  1. Aamodt A, Plaza E (1994) Case-Based Reasoning: foundational Issues, Methodological Variations, and System Approaches, AICOM. Vol. 7., March, pp 39–59.

    Google Scholar 

  2. Aarts E (2004) Ambient intelligence, Third International Conference, AH 2004, Eindhoven, The Netherlands. LNCS Springer Verlag. ISBN: 3-540-22895-0, p. 1.

    Google Scholar 

  3. Aiken J., Fishwick J.R., Lavender S.J., Barlow R., Moore G.F., Sessions H., Bernard S., Ras J. & Hardman-Mountford N.J. (Submitted). Validation of MERIS reflectance and chlorophyll during the BENCAL cruise October, 2002: preliminary validation and new products for phytoplankton functional types and photosynthetic parameters. International Journal of Remote Sensing.

    Google Scholar 

  4. Aiken J., Hardman-Mountford N., Ufermann S., Woolf D., Challenor P., Robinson I. and the CASIX team, (2005). Exploiting ENVISAT-ERS data for deriving air-sea fluxes of CO2, Proceedings of the Envisat Symposium, Salzburg, ESA publication, in press.

    Google Scholar 

  5. Bajo J, Corchado JM (2005) Evaluation and monitoring of the air-sea interaction using a CBR-Agents approach. ICCBR 2005. Lecture Notes in Computer Science 3620, pp 50–62.

    Article  Google Scholar 

  6. Bajo J, Corchado JM (2006) Multiagent architecture for monitoring the North-Atlantic carbon dioxide Exchange rate. CAEPIA 2005. Lecture Notes in Computer Science 4177, pp 321–330.

    Article  Google Scholar 

  7. Bajo J, Corchado JM, de Paz Y, de Paz JF, Martín Q. (2006) A multiagent recommending system for shopping centres. Proceedings of the workshop on Recommender Systems (ECAI 2006). Vol 1, pp 92–06.

    Google Scholar 

  8. Bauer B, Huget MP (2003) FIPA Modeling: Agent Class Diagrams.

    Google Scholar 

  9. Bellifime F, Poggi A, Rimasa G (2001) JADE: a FIPA2000 compliant agent development environement. Proceedings of the 5th international conference on autonomous agents (ACM).

    Google Scholar 

  10. Bergmann R, Wilke W (1996) On the role of abstraction in case-based reasoning. Lecture Notes in Artificial Intelligence, 1186, pp. 28–43. Springer Verlag.

    Google Scholar 

  11. Bratman ME, Israel D, Pollack ME (1988) Plans and resource-bounded practical reasoning. Computational Intelligence, 4., pp. 349–355.

    Article  Google Scholar 

  12. Bratman ME (1987) Intentions, Plans and Practical Reason. Harvard University Press, Cambridge, M.A.

    Google Scholar 

  13. Corchado JM, Laza R (2003) Constructing Deliberative Agents with Case-based Reasoning Technology, International Journal of Intelligent Systems. Vol 18, No. 12, December. pp. 1227–1241.

    Article  Google Scholar 

  14. Corchado JM, Lees B (2001) A Hybrid Case-based Model for Forecasting. Applied Artificial Intelligence. Vol 15, no. 2, pp. 105–127.

    Article  Google Scholar 

  15. Corchado JM, Aiken J, Corchado E, Lefevre N, Smyth T (2004) Quantifying the Ocean’s CO2 Budget with a CoHeL-IBR System. 7th European Conference on Case-based Reasoning, Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence 3155, Springer Verlag. pp. 533–546.

    Google Scholar 

  16. Corchado J M, Pavón J, Corchado E, Castillo LF (2005) Development of CBR-BDI Agents: A Tourist Guide Application. 7th European Conference on Case-based Reasoning 2004. Lecture Notes in Artificial Intelligence 3155, Springer Verlag. pp. 547–559.

    Google Scholar 

  17. De Paz Santana Y (2005) Mixture Weibull distribution using artificial neural networks with csnsurated data PHD thesis, Chapter 3.

    Google Scholar 

  18. DeLoach S (2001) Anlysis and Design using MaSE and AgentTool. Proceedings of the 12th Midwest Artificial Intelligence and Cognitive Science Conference (MAICS).

    Google Scholar 

  19. Dransfeld S., Tatnall A.R., Robinson I.S. and Mobley C.D. (2004). A comparison of Multi-layer Perceptron and multilinear regression algorithms for the inversion of synthetic ocean colour spectra. Int. J. Remote Sens. 25 (21): 4829–4834.

    Article  Google Scholar 

  20. Dransfeld S., Tatnall A.R., Robinson I.S. and Mobley C.D. (2005). Prioritizing ocean colour channels by neural network input reflectance perturbation. Int. J. Remote Sens. 26 (5): 1043–1048.

    Article  Google Scholar 

  21. EURESCOM (2001) MESSAGE: Methodology for engineering systems of software agents. Technical report P907-TI1, EURESCOM.

    Google Scholar 

  22. Feret MP, Glasgow JI (1994) Explanation-Aided Diagnosis for Complex Devices, Proceedings of the 12th National Conference an Artificial Intelligence, (AAAI-94), Seattle, USA, August 94.

    Google Scholar 

  23. FIPA Foundation for Intelligent Physical Agents. http://www.fipa.org.

  24. Fyfe C, Corchado ES (2002) Maximum Likelihood Hebbian Rules. European Symposium on Artificial Neural Networks. 2002.

    Google Scholar 

  25. Glez-Bedia M, Corchado JM, Corchado ES, Fyfe C (2002) Analytical Model for Constructing Deliberative Agents, Engineering Intelligent Systems, Vol 3, pp. 173–185.

    Google Scholar 

  26. Iglesias C, Garijo M, Gonzalez JC, Velasco JR (1998) Analysis and Design using MAS-CommonKADS. Inteligent Agents IV LNAI Volume 1365 Springer Verlag.

    Google Scholar 

  27. Kinny D, Georgeff M (1991). Commitment and effectiveness of situated agents. In: Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI’91), Sydney, Australia, pp. 82–88.

    Google Scholar 

  28. Kolodner J (1993) Case-based reasoning. Morgan Kaufmann.

    Google Scholar 

  29. Lavender, S.J., Pinkerton, M.H., Moore, G.F., Aiken, J and Blondeau-Patissier, D. (2005). Modification to the Atmospheric Correction of SeaWiFS Ocean Colour Images over Turbid Waters. Continental Shelf Research, 25, 539–555.

    Article  Google Scholar 

  30. Lefevre N, Aiken J, Rutllant J, Daneri G, Lavender S, Smyth T (2002) Observations of pCO2 in the coastal upwelling off Chile: Sapatial and temporal extrapolation using satellite data. Journal of Geophysical research. Vol. 107, no. 0.

    Google Scholar 

  31. Martín FJ, Plaza E, Arcos JL (1999) Knowledge and experience reuse through communications among competent (peer) agents. International Journal of Software Engineering and Knowledge Engineering, Vol. 9, No. 3, pp. 319–341.

    Article  Google Scholar 

  32. Montaño Moreno JJ, Palmer Pol A (2002) Artificial Neural Networks, opening the black box. Metodología de las Ciencias del Comportamiento 4(1) 77–93.

    Google Scholar 

  33. Nwana HS, Ndumu DT, Lee LC, Collins JC (1999) ZEUS: A Toolkit for Building Distributed Multi-Agent Systems. Applied Artificial Intelligence Jounal, vol 1, no 13, pp. 129–185.

    Google Scholar 

  34. Odell J, Levy R, Nodine M (2004) FIPA Modeling TC: Agent Class Superstructure Metamodel. FIPA meeting and interim work.

    Google Scholar 

  35. Odell J, Huget MP (2003) FIPA Modeling: Interaction Diagrams.

    Google Scholar 

  36. Olivia C, Chang CF, Enguix CF, Ghose AK (1999) Case-Based BDI Agents: An Effective Approach for Intelligent Search on the World Wide Web, AAAI Spring Symposium on Intelligent Agents, 22–24.

    Google Scholar 

  37. OMG Unified Modelling Language Specification. Version 1.3. http://www.omg.org.

  38. O’Reilly J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Carder, S.A. Garver, M. Kahru, C. McClain, 1998. Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research-Oceans, 103(11), 24937–24953

    Article  Google Scholar 

  39. Perner P (1998) Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation. Lecture Notes in Computer Science 1488, pp 251–261.

    Google Scholar 

  40. Perner P (2001) Why Case-Based Reasoning Is Attractive for Image Interpretation. Lecture Notes in Computer Science 2080, pp 27–43.

    Google Scholar 

  41. Pokahr A, Braubach L, Lamersdorf W (2003) Jadex: Implementing a BDI-Infrastructure for JADE Agents, in: EXP - In Search of Innovation (Special Issue on JADE), Vol 3, Nr. 3, Telecom Italia Lab, Turin, Italy, September 2003, pp. 76–85. Jadex

    Google Scholar 

  42. Rao AS, Georgeff MP (1995) BDI Agents: From Theory to Practice. First International Conference on Multi-Agent Systems (ICMAS-95). San Franciso, USA.

    Google Scholar 

  43. Sarmiento JL, Dender M (1994) Carbon biogeochemistry and climate change. Photosynthesis Research, Vol. 39, pp. 209–234.

    Article  Google Scholar 

  44. Takahashi T, Olafsson J, Goddard JG, Chipman DW, Sutherland SC (1993) Seasonal Variation of CO2 and nutrients in the High-latitude surface oceans: a comparative study. Global biochemical Cycles. Vol. 7, No. 4. pp. 843–878.

    Article  Google Scholar 

  45. The Beam Project. http://www.brockmann-consult.de/beam/.

  46. Wendler J, Lenz M (1998) CBR for Dynamic Situation Assessment in an Agent-Oriented Setting. Proc. AAAI-98 Workshop on CBR Integrations. Madison (USA).

    Google Scholar 

  47. Wooldridge M, Jennings NR (1995) Agent Theories, Architectures, and Languages: a Survey. In: Wooldridge and Jennings, editors, Intelligent Agents, Springer-Verlag, pp. 1–22.

    Google Scholar 

  48. Wooldridge M, Jennings NR, Kinny D (2000) The Gaia Methodology for Agent-Oriented Analysis and Design. Journal of Autonomous Agents and Multi-Agent Systems, 3 (3). pp. 285–312.

    Article  Google Scholar 

  49. Zeichen M.M., Robinson I.S. (2004). Detection and monitoring of algal blooms using SeaWiFS imagery (vol 25, pg 1389, 2004). Int. J. Remote Sens. 25 (9): 1797–1798.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Departamento Informática y Automática, Universidad de Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Spain

    J. M. Corchado

  2. Centre for Air-Sea Interactions and fluxes, Plymouth Marine Laboratory, Prospect Place, Plymouth, PL1 3 DH, UK

    J. Aiken

  3. Universidad Pontificia de Salamanca, Compañía 5, 37002, Salamanca, Spain

    J. Bajo

Authors
  1. J. M. Corchado
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  2. J. Aiken
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  3. J. Bajo
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Arno-Nitzsche-Str.43, 04277, Leipzig, Germany

    Petra Perner

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Corchado, J.M., Aiken, J., Bajo, J. (2008). A CBR Agent for Monitoring the Carbon Dioxide Exchange Rate from Satellite Images. In: Perner, P. (eds) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73180-1_8

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  • DOI: https://doi.org/10.1007/978-3-540-73180-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73178-8

  • Online ISBN: 978-3-540-73180-1

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