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A Weather Forecasting System Using Intelligent BDI Multiagent-Based Group Method of Data Handling

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Book cover Hard and Soft Computing for Artificial Intelligence, Multimedia and Security (ACS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 534))

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Abstract

In the article the concept of analysis of complex processes based on the multi-agent platform is presented. The analysis based on Group Method of Data Handling is employed to construct a model of the analysed process. The model is used to predict future development of the process. Employed multi-agent platform composed of BDI agents provides intelligent distributed computational environment. The description of this approach, case study examining various prediction model criterion and evaluationally summary of the approach is provided in the article.

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Notes

  1. 1.

    The forecasted value was closest to the actual value.

References

  1. Subagdja, B., Sonenberg, L., Rahwan, I.: Intentional learning agent architecture. Auton. Agent. Multi-Agent Syst. 18(3), 417–470 (2009)

    Article  Google Scholar 

  2. Wobcke, W.: A logic of intention and action for regular BDI agents based on bisimulation of agent programs. Auton. Agent. Multi-Agent Syst. 29(4), 569–620 (2015)

    Article  Google Scholar 

  3. Wooldridge, M.: An Introduction to Multiagent Systems. Wiley, New York (2009)

    Google Scholar 

  4. Bellifemine, F., Caire, G., Grenwood, D.: Developing Multi-Agent Systems with JADE. Wiley, Chichester (2007)

    Book  Google Scholar 

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

  6. Rogoza, V., Zabłocki, M.: Grid computing and cloud computing in scope of JADE and OWL based semantic agents–a survey. Przegląd Elektrotechniczny 90(2), 93–96 (2014)

    Google Scholar 

  7. Onwubolu, G.: GMDH-Methodology and Implementation in C. World Scientific Publishing Company (2014)

    Google Scholar 

  8. Khanmohammadi, S., Atashkari, K., Kouhikamali, R.: Exergoeconomic multi-objective optimization of an externally fired gas turbine integrated with a biomass gasifier. Appl. Therm. Eng. 91, 848–859 (2015)

    Article  Google Scholar 

  9. Shirmohammadi, R., Ghorbani, B., Hamedi, M., Hamedi, M., Romeo, L.M.: Optimization of mixed refrigerant systems in low temperature applications by means of group method of data handling (GMDH). J. Nat. Gas Sci. Eng. 26, 303–312 (2015)

    Article  Google Scholar 

  10. Najafzadeh, M., Barani, G., Hessami-Kermani, M.: Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds. Ocean Eng. 104, 387–396 (2015)

    Article  Google Scholar 

  11. Kondo, T., Ueno, J., Takao, S.: Medical image diagnosis of liver cancer by hybrid feedback GMDH-type neural network using principal component-regression analysis. Artif. Life Robot. 20(2), 145–151 (2015)

    Article  Google Scholar 

  12. Koo, B., Lee, H., Park, J.: Short-term electric load forecasting based on wavelet transform and GMDH. J. Electr. Eng. Technol. 10(3), 832–837 (2015)

    Article  MathSciNet  Google Scholar 

  13. Nunes, I., Lucena, C., Luck, M.: BDI4JADE: a BDI layer on top of JADE. In: International Workshop on Programming Multi-Agent Systems (ProMAS 2011), Taiwan (2011)

    Google Scholar 

  14. Nunes, I.: Improving the design and modularity of BDI agents with capability relationships. In: Dalpiaz, F., Dix, J., Riemsdijk, M.B. (eds.) EMAS 2014. LNCS (LNAI), vol. 8758, pp. 58–80. Springer, Heidelberg (2014). doi:10.1007/978-3-319-14484-9_4

    Google Scholar 

  15. Nunes, I., Luck, M.: Softgoal-based plan selection in model-driven BDI agents. In: The 13th International Conference on Autonomous Agents and Multiagent Systems, pp. 749–756 (2014)

    Google Scholar 

  16. Nunes, I.: Capability relationships in BDI agents. In: The 2nd International Workshop on Engineering Multi-Agent Systems (2014)

    Google Scholar 

  17. FIPA Communicative Act Library Specification. http://www.fipa.org/specs/fipa00037/

  18. FIPA SL Content Language Specification. http://www.fipa.org/specs/fipa00008/

  19. Morandini, M., Perini, A., Penserini, L., Marchetto, A.: Engineering requirements for adaptive systems. Requirements Eng. (2015)

    Google Scholar 

  20. Hilal, A.R., Basir, O.A.: A scalable sensor management architecture using BDI model for pervasive surveillance. IEEE Syst. J. 9(2), 529–541 (2015)

    Article  Google Scholar 

  21. Ren, Q., Bai, L., Biswas, S., Ferrese, F., Dong, Q.: A BDI multi-agent approach for power restoration. Paper presented at the 7th International Symposium on Resilient Control Systems, ISRCS 2014 (2014)

    Google Scholar 

  22. Maruf, M.N.I., Hurtado Munoz, L.A., Nguyen, P.H., Lopes Ferreira, H.M., Kling, W.L.: An enhancement of agent-based power supply-demand matching by using ANN-based forecaster. Paper presented at the 2013 4th IEEE/PES Innovative Smart Grid Technologies Europe, ISGT Europe 2013 (2013)

    Google Scholar 

  23. Linghu, B., Chen, F.: An intelligent multi-agent approach for flood disaster forecasting utilizing case based reasoning. Paper presented at the Proceedings - 2014 5th International Conference on Intelligent Systems Design and Engineering Applications, ISDEA 2014, pp. 182–185 (2014)

    Google Scholar 

  24. Elamine, D.O., Nfaoui, E.H., Jaouad, B.: Multi-agent architecture for smart micro-grid optimal control using a hybrid BP-PSO algorithm for wind power prediction. Paper presented at the 2014 2nd World Conference on Complex Systems, WCCS 2014, pp. 554–560 (2014)

    Google Scholar 

  25. Mogles, N., Ramallo-González, A.P., Gabe-Thomas, E.: Towards a cognitive agent-based model for air conditioners purchasing prediction. Paper presented at the Procedia Computer Science, pp. 463–472 (2015)

    Google Scholar 

  26. Ivakhnenko, A., Ivakhnenko, G.: Problems of further development of the group method of data handling algorithms. Pattern Recogn. Image Anal. 10(2), 187–194 (2000)

    Google Scholar 

  27. Ivakhnenko, A., Ivakhnenko, G., Mueller, J.: Self-organization of neural network with active neurons. Pattern Recogn. Image Anal. 4(2), 185–196 (1999)

    Google Scholar 

  28. Ivakhnenko, A.G., Ivakhnenko, G.A., Andrienko, N.M.: Inductive computer advisor for current forecasting of Ukraine’s macroeconomy. Syst. Anal. Model. Simul. (1998)

    Google Scholar 

  29. Ivakhnenko, G.A.: Model-free analogues as active neurons for neural networks self-organization. Control Syst. Comput. 2, 100–107 (2003)

    Google Scholar 

  30. Wiliński, A.: GMDH-metody grupowania argumentów w zadaniach zautomatyzowanej predykcji zachowań rynków finansowych (2009)

    Google Scholar 

  31. Bratman, M.E.: Intention, Plans, and Practical Reason. CSLI Publications, Stanford (1987)

    Google Scholar 

  32. Rao, A., Georgeff, M.: Modeling rational agents within a BDI architecture. In: Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning, pp. 473–484 (1991)

    Google Scholar 

  33. d’Inverno, M., Kinny, D., Luck, M., Wooldridge, M.: A formal specification of dMARS. In: Agent Theories, Architectures, and Languages, pp. 155–176 (1997)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, ul. Żołnierska 52, 71-210, Szczecin, Poland

    W. Rogoza & M. Zabłocki

Authors
  1. W. Rogoza
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  2. M. Zabłocki
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Corresponding author

Correspondence to W. Rogoza .

Editor information

Editors and Affiliations

  1. Graduate School of Science and Engineeri, Ehime University Graduate School of Science and Engineeri, Ehime, Japan

    Shin-ya Kobayashi

  2. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Andrzej Piegat

  3. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Jerzy Pejaś

  4. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Imed El Fray

  5. Polish Academy of Sciences, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Rogoza, W., Zabłocki, M. (2017). A Weather Forecasting System Using Intelligent BDI Multiagent-Based Group Method of Data Handling. In: Kobayashi, Sy., Piegat, A., Pejaś, J., El Fray, I., Kacprzyk, J. (eds) Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. ACS 2016. Advances in Intelligent Systems and Computing, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-48429-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-48429-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48428-0

  • Online ISBN: 978-3-319-48429-7

  • eBook Packages: EngineeringEngineering (R0)

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