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Performance Evaluation of the Customer Relationship Management Agent’s in a Cognitive Integrated Management Support System

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Transactions on Computational Collective Intelligence XVIII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9240))

Abstract

The biggest problem currently, turns out to be the processing of unstructured knowledge in integrated management support systems. Note that knowledge contained in these systems is normally structuralized and the systems employ various methods for processing structuralized knowledge. However, in contemporary companies, unstructured knowledge is essential, mainly due to the possibility to obtain better flexibility and competitiveness of the organization. The users’ opinions about products can serve as example. Therefore, unstructured knowledge supports structuralized knowledge to a high degree. This paper presents the issues related to the sentiment analysis of customers’ opinions performed by Customer Relationship Management agent running in multi-agent Cognitive Integrated Management Information System. This system is an application of computational collective intelligence and allows for supporting the management processes related with all the domain of enterprise’s functioning. The agents are based on the Learning Intelligent Distribution Agent cognitive architecture, described shortly in the first part of the paper. Next, the logical architecture of Cognitive Integrated Management Information System are described. The main part of article presents issues related to functionality and implementation of Customer Relationship Management agent aims to sentiment analysis. The results of research experiment, aims to performance evaluation, are presented at the last part of article.

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References

  1. Better execute your business strategies – with our enterprise resource planning (ERP) solution, 28 Apr 2014. http://www.sap.com/pc/bp/erp/software/overview.html

  2. Bytniewski, A. (ed.): Architektura zintegrowanego systemu informatycznego zarządzania. Wydawnictwo AE we Wrocławiu. Wrocław (2005) (in Polish)

    Google Scholar 

  3. Cognitive computing research group, 20 Apr 2014. http://ccrg.cs.memphis.edu/

  4. Duch, W., Oentaryo, R.J., Pasquier, M.: Cognitive architectures: where do we go from here? In: Wang, P., Goertzel, P., Franklin, S. (eds.) Frontiers in Artificial Intelligence and Applications, vol. 171, pp. 122–136. IOS Press, Amsterdam (2008)

    Google Scholar 

  5. Davenport, T.: Putting the enterprise into the enterprise system. Harvard Business Review, pp. 121–131 (1998)

    Google Scholar 

  6. Franklin, S., Patterson, F.G.: The LIDA architecture: adding new modes of learning to an intelligent, autonomous, software agent. In: Proceedings of the International Conference on Integrated Design and Process Technology. Society for Design and Process Science, San Diego, CA (2006)

    Google Scholar 

  7. Goertzel, B.: OpenCogPrime: a cognitive synergy based architecture for embodied general intelligence. In: Proceedings of ICCI-2009 (2009)

    Google Scholar 

  8. Hecht-Nielsen, R.: Confabulation Theory: The Mechanism of Thought. Springer, Heidelberg (2007)

    Google Scholar 

  9. Hernes, M., Nguyen, N.T.: Deriving consensus for hierarchical incomplete ordered partitions and coverings. J. Univ. Comput. Sci. 13(2), 317–328 (2007)

    Google Scholar 

  10. Hernes, M., Matouk, K.: Knowledge conflicts in business intelligence systems. In: Proceedings of Federated Conference Computer Science and Information Systems, pp. 1253–1258. Kraków (2013)

    Google Scholar 

  11. Hensinger, A., Thome, M., Wright, T.: Cougaar: a scalable, distributed multi-agent architecture. In: IEEE International Conference on Systems, Man and Cybernetics (2004)

    Google Scholar 

  12. Hofstadter, D.R., Mitchell, M.: The copycat project: a model of mental fluidity and analogy-making. In: Hofstadter, D., Fluid Analogies Research Group (eds.) Fluid Concepts and Creative Analogies, Chapter 5. Basic Books, New York (1995)

    Google Scholar 

  13. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) Machine Learning: ECML-98. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  14. Katarzyniak, R.: Grounding modalities and logic connectives in communicative cognitive agents. In: Nguyen, N.T. (ed.) Intelligent Technologies for Inconsistent Knowledge Processing, pp. 21–37. Advanced Knowledge International, Adelaide, Australia (2004)

    Google Scholar 

  15. Laird, J.E.: Extending the SOAR cognitive architecture. In: Wang, P., Goertzel, P., Franklin, S. (eds.) Frontiers in Artificial Intelligence and Applications, vol. 171, pp. 224–235. IOS Press, Amsterdam (2008)

    Google Scholar 

  16. Banaszak, Z., Klos, S., Mleczko, J.: Integrated management systems. Management and engeeniering of manufacturing. Polskie Wydawnictwo Ekonomiczne, Warszawa (2011)

    Google Scholar 

  17. Nazemi, E., Tarokh, M.J., Djavanshir, G.R.: ERP: a literature survey. Int. J. Adv. Manuf. Technol. 61, 999–1018 (2012)

    Article  Google Scholar 

  18. Nguyen, N.T.: Inconsistency of knowledge and collective intelligence. Cybern. Syst. 39(6), 542–562 (2008)

    Article  MATH  Google Scholar 

  19. Nguyen, N.T.: Metody wyboru consensusu i ich zastosowanie w rozwiązywaniu konfliktów w systemach rozproszonych. Wroclaw University of Technology Press (2002) (in Polish)

    Google Scholar 

  20. Pham, L.V., Pham, S.B.: Information extraction for Vietnamese real estate advertisements. In: Fourth International Conference on Knowledge and Systems Engineering (KSE), Danang (2012)

    Google Scholar 

  21. Plikynas, D.: Multiagent based global enterprise resource planning: conceptual view. Wseas Trans. Bus. Econ. 5(6), 372–382 (2008)

    Google Scholar 

  22. Rohrer, B.: An implemented architecture for feature creation and general reinforcement learning. In: Workshop on Self-Programming in AGI Systems, Fourth International Conference on Artificial General Intelligence, Mountain View, CA, 11 Apr 2014. http://www.sandia.gov/rohrer/doc/Rohrer11ImplementedArchitectureFeature.pdf

  23. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR), New York 34(1), 1–47 (2002)

    Article  Google Scholar 

  24. Singhal, A.: Modern information retrieval: a brief overview. Bull. IEEE Comput. Soc. Tech. Comm. Data Eng. 24(4), 35–43 (2001)

    Google Scholar 

  25. Sobieska-Karpińska, J., Hernes, M.: Consensus determining algorithm in multiagent decision support system with taking into consideration improving agent’s knowledge. In: Proceedings of Federated Conference Computer Science and Information Systems, pp. 1035–1040 (2012)

    Google Scholar 

  26. Soderland, S.: Learning information extraction rules from semi-structured and free text. Mach. Learn. 34(1–3), 233–272 (1999)

    Article  MATH  Google Scholar 

  27. Sołdacki, P.: Zastosowania metod płytkiej analizy tekstu do przetwarzania dokumentów w języku polskim. Praca Doktorska Politechniki Warszawskiej, Warszawa (2006). (in Polish)

    Google Scholar 

  28. Tomassen, S.L.: Semi-automatic generation of ontologies for knowledge-intensive CBR. Norwegian University of Science and Technology (2002)

    Google Scholar 

  29. Tran, C.: Cognitive information processing. Vietnam J. Comput. Sci. 1(4), 207–218 (2014). Springer, http://link.springer.com/article/10.1007/s40595-014-0019-4

    Article  Google Scholar 

  30. Trandabăţ, D.: Using semantic roles to improve summaries. In: Proceedings of the 13th European Workshop on Natural Language Generation (ENLG 2011), Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 164–169 (2011)

    Google Scholar 

  31. Vlas, R.E., Robinson, W.N.: Two rule-based natural language strategies for requirements discovery and classification in open source software development projects. J. Manag. Inf. Syst. 28(4), 11–38 (2012)

    Article  Google Scholar 

  32. Wang, P.: Rigid flexibility. The Logic of Intelligence, vol. 34. Springer, Netherlands (2006)

    MATH  Google Scholar 

  33. Wawer, A.: Mining opinion attributes from texts using multiple kernel learning. In: IEEE 11th International Conference on Data Mining Workshops (2011)

    Google Scholar 

  34. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(3), 399–433 (2009)

    Article  Google Scholar 

  35. Xpertis – intelligents systems of enterprise manufacturing, Macrologic, 15 Apr 2014. http://www.macrologic.pl/rozwiazania/erp

  36. Zenkin, A.: Intelligent control and cognitive computer graphics. In: IEEE International Symposium on Intelligent Control, pp. 366-371, Montreal, California (1995)

    Google Scholar 

  37. Zhang, C., Zhang, X., Jiang, W., Shen, Q., Zhang, S.: Rule-based extraction of spatial relations in natural language text. In: International Conference on Computational Intelligence and Software Engineering (2009)

    Google Scholar 

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Acknowledgement

This research was financially supported by the National Science Center (decision No. DEC-2013/11/D/HS4/04096).

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Correspondence to Marcin Hernes .

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Hernes, M. (2015). Performance Evaluation of the Customer Relationship Management Agent’s in a Cognitive Integrated Management Support System. In: Nguyen, N. (eds) Transactions on Computational Collective Intelligence XVIII. Lecture Notes in Computer Science(), vol 9240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48145-5_5

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  • DOI: https://doi.org/10.1007/978-3-662-48145-5_5

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