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Multi-agent Architecture for Intelligent Insurance Systems

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Distributed Computing and Artificial Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 217))

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Abstract

Modern insurance information systems need intelligence to provide new functions that till now as a rule have been carried out by humans. Introduction of intelligent mechanisms into information systems allows the insurance companies to automate processes in the insurance business and achieve two benefits. Firstly, the amount of work done by humans is reduced and secondly more services can be provided to customers electronically, which increases the level of customer service. Additionally, insurance information systems need to communicate with many other systems to get the needed data. These demands fit the characteristics of intelligent agents. Thus the paper proposes to implement an insurance information system as a multi-agent system using intelligent agents to realize the modules of insurance information systems. A novel multi-agent architecture for insurance information system development is proposed.

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References

  1. Demazeau, Y., Pavón, J., Corchado, J.M., Bajo, J. (eds.): 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009). AISC, vol. 55, 589 p. Springer, Heidelberg (2009)

    Google Scholar 

  2. Fischer, K., Schillo, M., Siekmann, J.H.: Holonic Multiagent Systems: A Foundation for the Organisation of Multiagent Systems. In: Mařík, V., McFarlane, D.C., Valckenaers, P. (eds.) HoloMAS 2003. LNCS (LNAI), vol. 2744, pp. 71–80. Springer, Heidelberg (2003)

    Google Scholar 

  3. Uhanova, M., Novitsky, L.: Application of Modeling and Internet Technologies in Marine Insurance Business Processes. In: Proceedings of 4th Int. Conference on Computer and IT Applications in the Maritime Industry, COMPIT 2005, Hamburg, pp. 34–43 (2005)

    Google Scholar 

  4. Novickis, L., et al.: Intelligent agents, modelling and web Technologies based development of distributed insurance software. ERDF Project report, 70 p. (2011) (in Latvian)

    Google Scholar 

  5. Wooldridge, M.: An Introduction to MultiAgent Systems, 2nd edn., 365 p. John Wiley & Sons (2009)

    Google Scholar 

  6. Russell, S., Norvig, P.: Artificial Intelligence. A Modern Approach, 2nd edn., 1112 p. Pearson Education, Upper Saddle River (2003)

    Google Scholar 

  7. Grundspenkis, J., Anohina, A.: Agents in Intelligent Tutoring Systems: State of the Art. In: Scientific Proceedings of Riga Technical University, Computer Science. Applied Computer Systems. 5th series, vol. 22, pp. 110–121. RTU Publishing, Riga (2005, 2009)

    Google Scholar 

  8. Lavendelis, E., Grundspenkis, J.: Open Holonic Multi-Agent Architecture for Intelligent Tutoring System Development. In: Proceedings of IADIS Int. Conference, Intelligent Systems and Agents 2008, Amsterdam, The Netherlands, pp. 100–108 (2008)

    Google Scholar 

  9. Lavendelis, E., Grundspenkis, J.: MASITS – A Multi-Agent Based Intelligent Tutoring System Development Methodology. In: Proceedings of IADIS International Conference, Intelligent Systems and Agents 2009, Algarve, Portugal, pp. 116–124 (2009)

    Google Scholar 

  10. Jekabsons, G.: Adaptive Basis Function Construction: an approach for adaptive building of sparse polynomial regression models. In: Zhang, Y. (ed.) Machine Learning, pp. 127–156. In-Tech (2010)

    Google Scholar 

  11. Jekabsons, G., Lavendels, J.: Polynomial regression modelling using adaptive construction of basis functions. In: Proceedings of IADIS International Conference, Applied Computing 2008, Mondragon unibertsitatea, Algarve, Portugal, pp. 269–276 (2008)

    Google Scholar 

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Correspondence to Egons Lavendelis .

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Lavendelis, E. (2013). Multi-agent Architecture for Intelligent Insurance Systems. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_53

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  • DOI: https://doi.org/10.1007/978-3-319-00551-5_53

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00550-8

  • Online ISBN: 978-3-319-00551-5

  • eBook Packages: EngineeringEngineering (R0)

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