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Consensus Theory for Cognitive Agents’ Unstructured Knowledge Conflicts Resolving in Management Information Systems

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Transactions on Computational Collective Intelligence XXXII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 11370))

Abstract

Management information systems of distributed nature, play a vital role in any kind of business organizations’ activity. The multi-agent systems, based on cognitive agent architecture, deserve special attention in this class of systems. They allow not only to access to the information and quick search for interesting us information, its analysis and drawing conclusions, but also, in addition to responding to stimuli from the environment, have the cognitive ability to learning through empirical experience gained through direct interaction with the environment. It, in turn, allows for the automatic generation of variants of decisions and, in many cases, even taking and putting into action the decisions. The big problem currently, however, turns out to be the processing of unstructured knowledge in systems of this kind. In contemporary companies, unstructured knowledge is essential, mainly due to the possibility to obtain better flexibility and competitiveness of the organization. Therefore, unstructured knowledge supports structured knowledge to a high degree. Simultaneously, one must note that the most prevailing phenomenon is a conflict in unstructured knowledge. It is extremely difficult to resolve conflicts of this kind properly. However, it is also very important, since it can improve the operation of management information system and, consequently, help the organization that employs the system become more flexible and competitive.

The main aim of this work is to develop a formal method to resolve conflicts in unstructured knowledge of cognitive agents in management information systems employing the consensus theory. The first part of this work presents an analysis problems related to management information systems and unstructured knowledge processing in these systems. Next, the cognitive agents are characterized with particular emphasis on unstructured knowledge processing. The use of consensus theory in unstructured knowledge conflicts resolving have been characterized in the third part of the work. The last part presents the developed method for cognitive agents’ knowledge conflicts resolving. The correctness of the method was verified using the prototypes of the agents helping to invest in the Forex market and processing user opinions about products and services.

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Notes

  1. 1.

    The reflections in this paper concern, in the wide scope, integrated, multi-agent MISs.

  2. 2.

    Especially with: Department of Information Systems Wrocław University of Science and Technology, Cognitive Computing Research Group The University of Memphis.

  3. 3.

    Traditional MIS are considered as MIS, which systems that do not contain artificial intelligence tools.

  4. 4.

    The detail of CIMIS functioning has been described, for example, in [52,53,54].

  5. 5.

    Opinion polarity is used to differentiate between negative and positive opinions; some sources refer to the process as sentiment analysis.

  6. 6.

    Technical analysis examines current rates and turnover against points of reference and historical data to establish values of assorted indices. This type of analysis can be employed even when without knowledge on the issuer of securities.

  7. 7.

    Fundamental analysis involves the study of a company or other issuer’s financial position, their strengths and weaknesses, and perspectives for development. It serves to address the question on the viability of investing in specific type of sectrities (this can also be done across whole economies, as in the case of currency exchange rates.

  8. 8.

    Behavioural analysis involves the study of behaviours and preferences of financial market participants.

  9. 9.

    In this work we consider mainly methods related to third generation of artificial intelligence. There also methods related to previous generations of AI, such as rules, frames, statements.

  10. 10.

    An algebraic model for representing text documents (and any objects, in general) as vectors of identifiers, such as, for example, index terms [83].

  11. 11.

    See Sect. 2 for a detailed review of unstructured knowledge representation methods and techniques.

  12. 12.

    A teacher can be a human or a computer program that confirms the correctness of learning of the cognitive agent.

  13. 13.

    A structure is a distribution of elements and a set of relations among them, characteristic for a given system as a whole, in other words it is a set of features of a given object.

  14. 14.

    Temporal data base is a base where the time of an event and the time of saving data to base play an important role.

  15. 15.

    Data replication is storing the same data on different servers to ensure the reliability of data read and save.

References

  1. Kisielnicki, J.: Management information systems. Placet, Warszawa (2013). (in Polish)

    Google Scholar 

  2. Wormell, I.: Databases as analytical tools. In: Dekker, M. (ed.) Encyclopedia of Library and Information Science, New York, vol. 70, no. 33, pp. 77–92 (2000)

    Google Scholar 

  3. Zins, C.: Conceptual approaches for defining data, information, and knowledge. J. Am. Soc. Inf. Sci. 58, 479–493 (2007). https://doi.org/10.1002/asi.20508

    Article  Google Scholar 

  4. Coulouris, G., Dollimore, J., Kindberg, T.: Distributed Systems: Concepts and Design, 5th edn. Pearson, London (2011)

    MATH  Google Scholar 

  5. Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison Wesley, New York (1999)

    Google Scholar 

  6. Wooldridge, M.: An Introduction to MultiAgent Systems. Wiley, Hoboken (2002). p. 366

    Google Scholar 

  7. Nguyen, N.T.: Methods for deriving consensus and their application in conflict resolving in distributed systems. PWr Printing House, Wroclaw (2002). (in Polish)

    Google Scholar 

  8. 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 

  9. Bush, P.: Tacit Knowledge in Organizational knowledge. IGI Global, Hershey, New York (2008)

    Book  Google Scholar 

  10. Griffin, R.W.: Management, 11th edn. South-Western College Pub, Chula Vista (2012)

    Google Scholar 

  11. Drucker, P.F.: Management Challenges for 21st Century. Harper Business, New York (2000)

    Google Scholar 

  12. Duan, H., Zheng, Y.: A study on features of the CRFs-based Chinese named entity recognition. Int. J. Adv. Intell. Inform. 3(2), 287–294 (2011)

    Google Scholar 

  13. Girdhar, J.: Management Information Systems. Oxford University Press, Oxford (2013)

    Google Scholar 

  14. Laudon, K.C., Laudon, J.P.: Management Information Systems: Managing the Digital Firm, 14th edn. Pearson, London (2015)

    MATH  Google Scholar 

  15. Burstein, F., Holsapple, C.W.: Handbook on Decision Support Systems. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-48713-5

    Book  Google Scholar 

  16. Kendal, S.L., Creen, M.: An Introduction to Knowledge Engineering. Springer, London (2007). https://doi.org/10.1007/978-1-84628-667-4

    Book  MATH  Google Scholar 

  17. Adamczewski, P.: Evolution in ERP-expanding functionality by bi-modules in knowledge-based management systems. In: Kubiak, B.F., Korowicki, A. (eds.) Information Management. Gdansk University Press, Gdańsk (2009)

    Google Scholar 

  18. Nycz, M.: Business intelligence as the exemplary modern technology influencing on the development on the enterprise. In: Kubiak, B.F., Korowicki, A. (eds.) Information Management. Gdansk University Press, Gdańsk (2009)

    Google Scholar 

  19. Sapkota, B., Roman, D., Kruk, S.R., Fensel, D.: Distributed web service discovery architecture. In: Advanced International Conference on Telecommunications and International Conference on Internet and Web Applications and Services (AICT-ICIW 2006), p. 136 (2006)

    Google Scholar 

  20. Ferreira, C.: Supporting unified distributed management and autonomic decisions: design, implementation and deployment. J. Netw. Syst. Manag. 25, 416–456 (2017). https://doi.org/10.1007/s10922-016-9398-4

    Article  Google Scholar 

  21. Frank, L., Pedersen, R.U.: Integrated distributed/mobile logistics management. In: Hameurlain, A., Küng, J., Wagner, R. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems V. LNCS, vol. 7100, pp. 206–221. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28148-8_9

    Chapter  Google Scholar 

  22. Turban, E., King, D., Lee, J.K., Liang, T.-P., Turban, D.C.: Mobile commerce and ubiquitous computing. In: Turban, E., King, D., Lee, J.K., Liang, T.-P., Turban, D.C. (eds.) Electronic Commerce. STBE, pp. 257–308. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-10091-3_6

    Chapter  Google Scholar 

  23. Markoska, K., Ivanochko, I., Gregus ml., M.: Mobile banking services—business information management with mobile payments. In: Kryvinska, N., Gregus, M. (eds.) Agile Information Business. FSM, pp. 125–175. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3358-2_5

    Chapter  Google Scholar 

  24. Bytniewski, A. (ed.): An Architecture of integrated management system. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław (2015). (in Polish)

    Google Scholar 

  25. APICS Operations Management Body of Knowledge Framework, 3rd edn. http://www.apics.org/apics-for-individuals/apics-magazine-home/resources/ombok/apics-ombok-framework-table-of-contents/apics-ombok-framework-5.1. Accessed 10 Oct 2017

  26. Plikynas, D.: Multiagent based global enterprise resource planning: conceptual view. WSEAS Trans. Bus. Econ. 5(6), 31–123 (2008)

    Google Scholar 

  27. Davenport, T.: Putting the enterprise into the enterprise system. Harv. Bus. Rev. 76, 121–131 (1998)

    Google Scholar 

  28. Better execute your business strategies - with our enterprise resource planning (ERP) solution. http://www.sap.com/pc/bp/erp/software/overview.html. Accessed 28 Nov 2017

  29. Zhang, D.Z., Anosike, A.I., Lim, M.K., Akanle, O.M.: An agent-based approach for e-manufacturing and supply chain integration. Comput. Ind. Eng. 51(2), 343–360 (2006)

    Article  Google Scholar 

  30. Boella, G., Hulstijn, J., Van Der Torre, L.: Virtual organizations as normative multiagent systems. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, p. 192c (2005)

    Google Scholar 

  31. Hecker, A.: Knowledge beyond the individual? Making sense of a notion of collective knowledge in organization theory. Org. Stud. 33(3), 423–445 (2012)

    Article  Google Scholar 

  32. Amidon, D.: The Innovation Strategy for the Knowledge Economy. Heinemann, Butterworth (1997)

    Google Scholar 

  33. Jakubczyc, J., Mercier-Laurent, E., Owoc, M.: What is Knowledge Management?. KAM, Wroclaw (1999)

    Google Scholar 

  34. Mercier-Laurent, E.: Artificial intelligence for successful Kflow. In: Mercier-Laurent, E., Boulanger, D. (eds.) AI4KM 2015. IAICT, vol. 497, pp. 149–165. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55970-4_9

    Chapter  Google Scholar 

  35. Salojärvi, S., Furu, P., Sveiby, K.-E.: Knowledge management and growth in finnish SMEs. J. Knowl. Manag. 9(2), 103–122 (2005)

    Article  Google Scholar 

  36. Girard, J.P., Girard, J.L.: Defining knowledge management: toward an applied compendium. J. Appl. Knowl. Manag. 3(1), 14 (2015)

    Google Scholar 

  37. Davenport, T.: Enterprise 2.0: the new, new knowledge management? Harv. Bus. Rev. (2008). http://discussionleader.hbsp.com/davenport/2008/02/

  38. Newell, A., Shaw, J.C., Simon, H.A.: Report on a general problem-solving program. In: Proceedings of the International Conference on Information Processing, pp. 256–264 (1959)

    Google Scholar 

  39. Kingston, J., Shadbolt, N., Tate, A.: CommonKADS models for knowledge based planning. In: AAAI/IAAI, vol. 1, pp. 477–482 (1996)

    Google Scholar 

  40. Mercier-Laurent, E., Owoc, M.L., Boulanger, D. (eds.): AI4KM 2014. IAICT, vol. 469. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-28868-0

    Book  Google Scholar 

  41. Motta, E., Rajan, T., Eisenstadt, M.: A methodology and tool for knowledge acquisition in KEATS-2. In: Guida, G., Tasso C. (eds.) Topics in Expert Systems Design, North-Holland (1989)

    Google Scholar 

  42. Gruber, T.: Ontology. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-39940-9_3200

    Chapter  Google Scholar 

  43. Mercier-Laurent, E.: Innovation Ecosystems. Wiley, Hoboken (2011)

    Book  Google Scholar 

  44. Mercier-Laurent, E., Boulanger, D. (eds.): AI4KM 2012. IAICT, vol. 422. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54897-0

    Book  MATH  Google Scholar 

  45. Owoc, M.L., Marciniak, K.: Knowledge management as foundation of smart university. In: Proceedings of the Federated Conference on Computer Science and Information Systems, Kraków, pp. 1267–1272 (2013)

    Google Scholar 

  46. Nonaka, I., Takeuchi, H.: The Knowledge-Creating Company. How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York, Oxford (1995)

    Google Scholar 

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

    Google Scholar 

  48. Langley, P.: The changing science of machine learning. Mach. Learn. 82(3), 275–279 (2011)

    Article  MATH  Google Scholar 

  49. Sathish Babu, B., Venkataram, P.: Cognitive agents based authentication & privacy scheme for mobile transactions (CABAPS). Comput. Commun. 31(17), 4060–4071 (2008)

    Article  Google Scholar 

  50. 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 (2006)

    Google Scholar 

  51. Hernes, M., Bytniewski, A.: Towards big management. In: Król, D., Nguyen, N.T., Shirai, K. (eds.) ACIIDS 2017. SCI, vol. 710, pp. 197–209. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56660-3_18

    Chapter  Google Scholar 

  52. Bytniewski, A., Hernes, M.: The use of cognitive agents in the construction of an integrated information management system. In: Porębska-Miąc, T., Sroka, H. (eds.) Systemy Wspomagania Organizacji. Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, Katowice (2013). (in Polish)

    Google Scholar 

  53. Hernes, M.: A cognitive integrated management support system for enterprises. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS (LNAI), vol. 8733, pp. 252–261. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11289-3_26

    Chapter  Google Scholar 

  54. Hernes, M.: Performance evaluation of the customer relationship management agent’s in a cognitive integrated management support system. In: Nguyen, N.T. (ed.) Transactions on Computational Collective Intelligence XVIII. LNCS, vol. 9240, pp. 86–104. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48145-5_5

    Chapter  Google Scholar 

  55. Get ahead of the game with an enterprise resource planning system (ERP) from SAP. https://www.sap.com/products/enterprise-management-erp.html. Accessed 20 Nov 2017

  56. Microsoft Dynamics 365 for Finance and Operations, Enterprise edition. https://www.microsoft.com/en-us/dynamics365/operations. Accessed 20 Nov 2017

  57. Hernes, M., Matouk, K.: Knowledge conflicts in business intelligence systems. In: Annals of Computer Science and Information Systems, Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Kraków (2013)

    Google Scholar 

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

    Google Scholar 

  59. Pilipczuk, O., Eidenzon, D.: The application of cognitive computer graphics to economic data exploration. J. Autom. Mob. Robot. Intell. Syst. 7(3), 3–9 (2013)

    Google Scholar 

  60. Rosenberg, J., Mateos, A.: The Cloud at Your Service, 1st edn. Manning Publications, New York (2010)

    Google Scholar 

  61. Kubiak, B.F., Korowicki, A. (eds.): Information Management. Gdansk University Press, Gdańsk (2009)

    Google Scholar 

  62. Oxford dictionaries, knowledge. http://www.oxforddictionaries.com/definition/english/knowledge. Accessed 20 Nov 2017

  63. Owoc, M.L., Weichbroth, P., Zuralski, K.: Towards better understanding of context-aware knowledge transformation. In: Proceedings of FedCSIS 2017, pp. 1123–1126 (2017)

    Google Scholar 

  64. Dixon, N.: How to make use of your organization’s collective knowledge - accessing the knowledge of the whole organization - Part I (2011). http://www.nancydixonblog.com/2011/01/how-to-make-use-of-your-organizations-collective-knowledge-accessing-the-knowledge-of-the-whole-orga.html

  65. Chaffey, D., White, G.: Business Information Management. Prentice Hall, London (2011)

    Google Scholar 

  66. Kimmerle, J., Cress, U., Held, C.: The interplay between individual and collective knowledge: technologies for organisational learning and knowledge building. Knowl. Manag. Res. Pract. 8, 33–44 (2010). https://doi.org/10.1057/kmrp.2009.36

    Article  Google Scholar 

  67. Lindskog, H., Mercier-Laurent, E.: Knowledge management applied to electronic public procurement. In: Mercier-Laurent, E., Boulanger, D. (eds.) AI4KM 2012. IAICT, vol. 422, pp. 95–111. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54897-0_6

    Chapter  Google Scholar 

  68. Salaberry, M.R.: Declarative versus procedural knowledge. In: Liontas, J.I., DelliCarpini, M. (eds.) The TESOL Encyclopedia of English Language Teaching (2018). https://doi.org/10.1002/9781118784235.eelt0051

  69. Furmankiewicz, M., Sołtysik-Piorunkiewicz, A., Ziuziański, P.: Artificial intelligence and multi-agent software for e-health knowledge management system. Bus. Inform. 2(32), 51–63 (2014)

    Google Scholar 

  70. Karlsen, J.E.: Eur. J. Futures Res. 2, 40 (2014). https://doi.org/10.1007/s40309-014-0040-y

    Article  Google Scholar 

  71. Buzzetto-More, N.: Principles of Effective Online Teaching. Informing Science Press, Santa Rosa (2007)

    Google Scholar 

  72. Toffler, A.: Powershift: Knowledge, Wealth and Violence at the Edge of the 21st Century. Bantam Books, New York (1990)

    Google Scholar 

  73. Owoc, M.L. (eds.) Elements of expert systems. Wydawnictwo AE we Wrocławiu, Wrocław (2006). (in Polish)

    Google Scholar 

  74. Edvinsson, L., Kitts, B., Beding, T.: The next generation of IC measurement – the digital IC-landscape. J. Intell. Capital 1(3), 263–273 (2000). https://doi.org/10.1108/14691930010350819

    Article  Google Scholar 

  75. Chan, K.C., Mills, T.M.: Modeling competition over product life cycles. Asia-Pac. J. Oper. Res. 32(4) (2015)

    Google Scholar 

  76. Fikes, R., Kehler, T.: The role of frame-based representation in reasoning. Commun. ACM 28(9), 904–920 (1985). https://doi.org/10.1145/4284.4285

    Article  Google Scholar 

  77. Kadhim, M.A., Afshar Alam, M., Kaur, H.: A multi-intelligent agent for knowledge discovery in database (MIAKDD): cooperative approach with domain expert for rules extraction. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS (LNAI), vol. 8589, pp. 602–614. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09339-0_61

    Chapter  Google Scholar 

  78. Palit, I., Phelps, S., Ng, W.L.: Can a zero-intelligence plus model explain the stylized facts of financial time series data? In: Proceedings of the Eleventh International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), vol. 2, pp. 653–660. International Foundation for Autonomous Agents and Multiagent Systems, Valencia (2012)

    Google Scholar 

  79. Zhang, X.F., Wang, G.J., Meng, G.W.: Theory of truth degree based on the interval interpretation of first-order fuzzy predicate logic formulas and its application. Fuzzy Syst. Math. 20(2), 8–12 (2006)

    MathSciNet  MATH  Google Scholar 

  80. Wang, X.Z., An, S.F.: Research on learning weights of fuzzy production rules based on maximum fuzzy entropy. J. Comput. Res. Dev. 43(4), 673–678 (2006)

    Article  Google Scholar 

  81. Zhu, G.J., Xia, Y.M.: Research and practice of frame knowledge representation. J. Yunnan Univ. (Nat. Sci. Ed.) 28(S1), 154–157 (2006)

    Google Scholar 

  82. Zeng, Z.: Construction of knowledge service system based on semantic web. J. China Soc. Sci. Tech. Inf. 24(3), 336–340 (2005)

    Google Scholar 

  83. Castells, P., Fernandez, M., Vallet, D.: An adaptation of the vector-space model for ontology-based information retrieval. IEEE Trans. Knowl. Data Eng. 19(2), 261–272 (2007). https://doi.org/10.1109/tkde.2007.22

    Article  Google Scholar 

  84. Keikha, M., Razavian, N.S., Oroumchian, F., Razi, H.S.: Document representation and quality of text: an analysis. In: Berry, M.W., Castellanos, M. (eds.) Survey of Text Mining II. Springer, London (2008). https://doi.org/10.1007/978-1-84800-046-9_12

    Chapter  Google Scholar 

  85. Dudycz, H.: A topics map as a visual representation of economic knowledge. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław (2013). (in Polish)

    Google Scholar 

  86. Hofweber, T.: Logic and ontology. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (Summer 2018 Edition) (2018). https://plato.stanford.edu/archives/sum2018/entries/logic-ontology/

  87. Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. International Handbooks on Information Systems. Springer, Berlin, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_0

    Chapter  Google Scholar 

  88. Fensel, D.: Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce. Springer, New York (2001). https://doi.org/10.1007/978-3-662-09083-1

    Book  MATH  Google Scholar 

  89. Sowa, J.F.: Semantic Networks. http://www.jfsowa.com/pubs/semnet.htm. Accessed 20 Oct 2017

  90. Korczak, J., Dudycz, H., Dyczkowski, M.: Design of financial knowledge in dashboard for SME managers. In: Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, Kraków, pp. 1111–1118 (2013)

    Google Scholar 

  91. Burger, W., Burge, M.J.: Principles of Digital Image Processing: Fundamental Techniques. Springer, London (2009). https://doi.org/10.1007/978-1-84800-191-6

    Book  MATH  Google Scholar 

  92. Solomon, C.J., Breckon, T.P.: Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley, Hoboken (2010)

    Book  Google Scholar 

  93. Murat Tekalp, A.: Digital Video Processing, 2nd edn. Prentice Hall, Upper Saddle River (2015)

    Google Scholar 

  94. Newmarch, J.: OpenMAX video processing on the Raspberry Pi. In: Newmarch, J. (ed.) Raspberry Pi GPU Audio Video Programming. Apress, Berkeley (2017)

    Chapter  Google Scholar 

  95. Lin, H.-P., Hsieh, H.-Y.: On using digital speech processing techniques for synchronization in heterogeneous teleconferencing. In: Bartolini, N., Nikoletseas, S., Sinha, P., Cardellini, V., Mahanti, A. (eds.) QShine 2009. LNICST, vol. 22, pp. 679–695. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10625-5_43

    Chapter  Google Scholar 

  96. Baker, J., et al.: Developments and directions in speech recognition and understanding, part 1. IEEE Signal Process. Mag. 26(3), 75–80 (2009)

    Article  Google Scholar 

  97. Baldoni, M., Baroglio, C., Patti, V., Rena, P.: From tags to emotions: ontology-driven sentiment analysis in the social semantic web. Intelligenza Artificiale 6(1), 41–54 (2012)

    Article  Google Scholar 

  98. Potiopa, P.: Methods and tools for automatic text information processing and their use in the knowledge management process. Automatyka 15(2), 409–419 (2011). http://journals.bg.agh.edu.pl/AUTOMATYKA/2011-02/Auto40.pdf

    Google Scholar 

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

    Google Scholar 

  100. 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 

  101. Balke, W.T.: Introduction to information extraction: basic notions and current trends. Datenbank-Spektrum 12(2), 81–88 (2012)

    Article  Google Scholar 

  102. Konchady, M.: Text Mining Application Programming. Cengage Learning India Private Ltd., New Delhi (2009)

    Google Scholar 

  103. Duan, R., Zhang, M.: Design of web-based management information system for academic degree & graduate education. In: Wang, W., Li, Y., Duan, Z., Yan, L., Li, H., Yang, X. (eds.) QShine 2009. IFIP International Federation for Information Processing, vol. 252, pp. 218–226. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-75494-9_27

    Chapter  Google Scholar 

  104. Banko, M., Cafarella, M., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad (2007)

    Google Scholar 

  105. Bekkerman, R., McCallum, A.: Disambiguating web appearances of people in a social network. In: Proceedings of International Conference on World Wide Web (WWW), Chiba (2005)

    Google Scholar 

  106. Hassell, J., Aleman-Meza, B., Arpinar, I.B.: Ontology-driven automatic entity disambiguation in unstructured text. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 44–57. Springer, Heidelberg (2006). https://doi.org/10.1007/11926078_4

    Chapter  Google Scholar 

  107. Chaudhuri, S., Ganti, V., Xin, D.: Mining document collections to facilitate accurate approximate entity matching. In: Proceedings of International Conference on Very Large Data Bases (VLDB), vol. 2, no. 1, Lyon (2009)

    Google Scholar 

  108. Dong, X., Halevy, A., Madhavan, J.: Reference reconciliation in complex information spaces. In: Proceedings of ACM International Conference on Management of Data, Baltimore (2005)

    Google Scholar 

  109. Cimiano, P., Handschuh, S., Staab, S.: Towards the selfannotating web. In: Proceedings of International Conference on World Wide Web (WWW), New York (2004)

    Google Scholar 

  110. Stoica, E., Hearst, M., Richardson, M.: Automating creation of hierarchical faceted metadata structures. In: Proceedings of Human Language Technology Conference of the Association of Computational Linguistics, Rochester (2007)

    Google Scholar 

  111. Carlson, A., Betteridge, J., Wang, R.C.: Coupled semi-supervised learning for information extraction. In: WSDM 2010, 4–6 February, New York (2010)

    Google Scholar 

  112. Etzioni, O., et al.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165(1), 1–42 (2005)

    Article  MathSciNet  Google Scholar 

  113. Nenkova, A., McKeown, K.: Automatic summarization. Found. Trends Inf. Retr. 5(2–3), 103–233 (2011)

    Article  Google Scholar 

  114. Clahsen, F., Harald, C.: Grammatical Processing in Language Learners. Appl. Psycholinguist. 27, 3–42 (2006)

    Article  Google Scholar 

  115. Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345–420 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  116. Costa, F., Branco, A.: LXGram: a deep linguistic processing grammar for Portuguese. In: Pardo, T.A.S., Branco, A., Klautau, A., Vieira, R., de Lima, V.L.S. (eds.) PROPOR 2010. LNCS (LNAI), vol. 6001, pp. 86–89. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12320-7_11

    Chapter  Google Scholar 

  117. Pollard, C., Sag, I.: Head-Driven Phrase Structure Grammar. Chicago University Press and CSLI, Stanford (1994)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  121. Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. ACL, Stroudsburg (2004)

    Google Scholar 

  122. Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Stroudsburg (2002)

    Google Scholar 

  123. Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: EMNLP, Stroudsburg (2003)

    Google Scholar 

  124. Riloff, E.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, Stroudsburg (2003)

    Google Scholar 

  125. Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30586-6_53

    Chapter  Google Scholar 

  126. Michalski, R.S., Tecuci, G. (eds.): Machine Learning: A Multistrategy Approach, Volume IV. Morgan Kaufmann, ‎Burlington (1994)

    Google Scholar 

  127. Vetulani, Z., Vetulani G., Kochanowski, B.: Recent advances in development of a lexicon-grammar of Polish: PolNet 3.0. In: Calzolari, N., et al. (eds.) Proceedings of the Tenth International Conference on Language Resources and Evaluation, LREC 2016, pp. 2851–2854. European Language Resources Association, Paris (2016)

    Google Scholar 

  128. Jackson, R.G., et al.: Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project. BMJ Open 7, e012012 (2017). https://doi.org/10.1136/bmjopen-2016-012012

    Article  Google Scholar 

  129. Abramowicz, W., Bukowska, E., Filipowska, A.: Ensuring security through semantic monitoring of cyberspace. E-mentor 3(50), 11–17 (2013)

    Google Scholar 

  130. Baptista, P., Cunha, T.R., Gama, C., Bernardes, C.: A new and practical method to obtain grain size measurements in sandy shores based on digital image acquisition and processing. Sed. Geol. 282, 294–306 (2012)

    Article  Google Scholar 

  131. Juang, B.H., Rabiner, L.R.: Automatic speech recognition-a brief history of the technology development. http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/354_LALI-ASRHistory-final-10-8.pdf. Accessed 17 June 2017

  132. Wilpon, J., Gilbert, M.E., Cohen, J.: The business of speech technologies. In: Benesty, J., Sondhi, M.M., Huang, Y.A. (eds.) Springer Handbook of Speech Processing. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-49127-9_34

    Chapter  Google Scholar 

  133. Owoc, M.L., Ochmanska, M., Gladysz, T.: On principles of knowledge validation. In: Vermesan, A., Coenen, F. (eds.) Validation and Verification of Knowledge Based Systems. Springer, Boston (1999). https://doi.org/10.1007/978-1-4757-6916-6_2

    Chapter  Google Scholar 

  134. Suh, Y.H., Murray, T.J.: A tree-based approach for verifying completeness and consistency in rule-based systems. Expert Syst. Appl. 7(2), 199–220 (1994)

    Article  Google Scholar 

  135. Hernes, M., Sobieska-Karpińska, J.: Application of the consensus method in a multiagent financial decision support system. IseB 14(1), 167–185 (2016)

    Article  Google Scholar 

  136. Korczak, J., Hernes, M., Bac, M.: Risk avoiding strategy in multi-agent trading system. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Kraków, pp. 1131–1138 (2013)

    Google Scholar 

  137. Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer, London (2008). https://doi.org/10.1007/978-1-84628-889-0

    Book  MATH  Google Scholar 

  138. Mirkin, B.G., Shestakov, A.: Least square consensus clustering: criteria, methods, experiments. In: Serdyukov, P., et al. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 764–767. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36973-5_79

    Chapter  Google Scholar 

  139. Sobieska-Karpińska, J., Hernes, M.: Value of information in distributed decision support system. In: Pańkowska, M. (ed.) Infonomics for Distributed Business and Decision-Making Environments: Creating Information System Ecology. IGI Global, Hershey, New York (2009)

    Google Scholar 

  140. Albus, J.S., Barbera, A.J.: RCS: a cognitive architecture for intelligent multi-agent systems. Ann. Rev. Control 29(1), 87–99 (2005)

    Article  Google Scholar 

  141. Kollmann, S., Siafara, L.C., Schaat, S., Wendt, A.: Towards a cognitive multi-agent system for building control. Procedia Comput. Sci. 88, 191–197 (2016)

    Article  Google Scholar 

  142. Iantovics, B.: Cognitive medical multiagent systems. BRAIN. Broad Res. Artif. Intell. Neurosci. 1(1), 12–21 (2010). Happy BRAINew Year!

    Google Scholar 

  143. Acampora, G., Vitiello, A.: Learning of fuzzy cognitive maps for modelling gene regulatory networks through Big Bang-Big Crunch algorithm. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, pp. 1–6 (2015)

    Google Scholar 

  144. Glaser, N., Chevrier, V., Haton, J.P.: Multi-agent modeling for autonomous but cooperative robots. In: Proceedings of 1st DIMAS, Cracow, Poland, pp. 175–182 (1995)

    Google Scholar 

  145. Korczak, J., Lipiński, P.: Agent systems in decision support on the securities market. In: Stanek, S., Sroka, H., Paprzycki, M., Ganzha, M. (eds.) Rozwój informatycznych systemów wieloagentowych. Wydawnictwo Placet, Warszawa (2008)

    Google Scholar 

  146. 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 

  147. Goertzel, B., Wang, P.: Introduction: what is the matter here? In: Goertzel, B., Wang, P. (eds.) Foundations of Artificial General Intelligence. Atlantis Press, Paris (2012)

    Google Scholar 

  148. Duch, W.: Artificial Intelligence. Knowledge Representation II: Semantic Networks. https://www.fizyka.umk.pl/~duch/Wyklady/AI/AI06-1.ppt. Accessed 23 Jan 2017

  149. Douglas Bernheim, B., Rangel, A.: Behavioural public economics. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics, 2nd edn. Palgrave Macmillan, Basingstoke (2008)

    Google Scholar 

  150. Hawkins, J., Blakeslee, S.: On Intelligence: How a New Understanding of the Brain will Lead to the Creation of Truly Intelligent Machines. Times Books, New York (2004)

    Google Scholar 

  151. Hecht-Nielsen, R.: Confabulation Theory: The Mechanism of Thought. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-49605-2

    Book  Google Scholar 

  152. Laird, J.E.: Extending the SOAR cognitive architecture. In: Wang, P., Goertzel, B., Franklin, S. (eds.) Frontiers in Artificial Intelligence and Applications, vol. 171 (2008)

    Google Scholar 

  153. Kieras, D., Meyer, D.E.: An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Hum.-Comput. Interac. 12, 391–438 (1997)

    Article  Google Scholar 

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

    Google Scholar 

  155. Wang, P.: Rigid Flexibility. The Logic of Intelligence. Springer, Dordrecht (2006). https://doi.org/10.1007/1-4020-5045-3

    Book  MATH  Google Scholar 

  156. Langley, P.: An adaptive architecture for physical agents. In: Proceeding of the 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. IEEE Computer Society Press, Compiegne (2005)

    Google Scholar 

  157. Edelman, G.M.: Second Nature: Brain Science and Human Knowledge. Yale University Press, New Haven (2006)

    Google Scholar 

  158. 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. http://www.sandia.gov/rohrer/doc/Rohrer11ImplementedArchitectureFeature.pdf. Accessed 01 Apr 2017

  159. Anderson, J.R., Lebiere, C.: The Newell test for a theory of cognition. Behav. Brain Sci. 26, 587–601 (2003)

    Article  Google Scholar 

  160. Sun, R., Zhang, X.: Top-down versus bottom-up learning in cognitive skill acquisition. Cogn. Syst. Res. 5, 63–89 (2004)

    Article  Google Scholar 

  161. Goertzel, B.: OpenCogBot: achieving generally intelligent virtual agent control and humanoid robotics via cognitive synergy. In: Proceedings of ICAI 2010, Beijing (2010)

    Google Scholar 

  162. Nestor, A., Kokinov, B.: Towards active vision in the DUAL cognitive architecture. Int. J. Inf. Theor. Appl. 11, 9–15 (2004)

    Google Scholar 

  163. Just, M.A., Varma, S.: The organization of thinking: what functional brain imaging reveals about the neuroarchitecture of complex cognition. Cogn. Affect. Behav. Neurosci. 7, 153–191 (2007)

    Article  Google Scholar 

  164. Goertzel, B., et al.: An integrative methodology for teaching embodied non-linguistic agents, applied to virtual animals in second life. In: Wang, P., Goertzel, B., Franklin, S. (eds.) Artificial General Intelligence 2008. IOS Press, Amsterdam (2008)

    MATH  Google Scholar 

  165. 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 

  166. Cognitive Computing Research Group. http://ccrg.cs.memphis.edu/. Accessed 02 Nov 2017

  167. Katarzyniak, R.: Priming the modal language of communication in agent systems. Akademicka Oficyna Wydawnicza EXIT (2007). (in Polish)

    Google Scholar 

  168. Pulvermuller, F.: The Neuroscience of Language. On Brain Circuits of Words and Serial Order. Cambridge University Press, Cambridge (2003)

    Book  Google Scholar 

  169. Hernes, M.: The semantic method for agents’ knowledge representation in the cognitive integrated management information system. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Łódź (2015)

    Google Scholar 

  170. Dalkir, K.: Knowledge Management in Theory and Practice. Elsevier Inc., Jordan Hill, Oxford (2005). p. 330

    Google Scholar 

  171. Snaider, J., McCall, R., Franklin, S.: The LIDA framework as a general tool for AGI. In: Schmidhuber, J., Thórisson, Kristinn R., Looks, M. (eds.) AGI 2011. LNCS (LNAI), vol. 6830, pp. 133–142. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22887-2_14

    Chapter  Google Scholar 

  172. Hernes, M.: Using cognitive agents for unstructured knowledge management in a business organization’s integrated information system. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS (LNAI), vol. 9621, pp. 344–353. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49381-6_33

    Chapter  Google Scholar 

  173. Nguyen, N.T.: Conflicts of ontologies – classification and consensus-based methods for resolving. In: Gabrys, B., Howlett, R.J., Jain, Lakhmi C. (eds.) KES 2006. LNCS (LNAI), vol. 4252, pp. 267–274. Springer, Heidelberg (2006). https://doi.org/10.1007/11893004_34

    Chapter  Google Scholar 

  174. Ferber, J.: Multi-agent concepts and methodologies. In: Phan, D., Amblard, F. (eds.) Agent-Based Modelling and Simulation in the Social and Human Sciences. Bardwell Press, Oxford (2007)

    Google Scholar 

  175. Rosenfeld, A., Agmon, N., Maksimov, O., Kraus, S.: Intelligent agent supporting human-multi-robot team collaboration. Artif. Intell. 252, 211–231 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  176. Gaudel, R., Sebag, M.: Feature selection as a one-player game. In: ICML (2010)

    Google Scholar 

  177. Hemsley, G., Holm, A., Dodd, B.: Conceptual distance and word learning: patterns of acquisition in Samoan-English bilingual children. J. Child Lang. 40, 799–820 (2013). https://doi.org/10.1017/S0305000912000293

    Article  Google Scholar 

  178. Basheer, G.S., Ahmad, M.S., Tang, A.Y.C.: A framework for conflict resolution in multi-agent systems. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds.) ICCCI 2013. LNCS (LNAI), vol. 8083, pp. 195–204. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40495-5_20

    Chapter  Google Scholar 

  179. Dyk, P., Lenar, M.: Applying negotiation methods to resolve conflicts in multi-agent environments. In: Zgrzywa, A. (ed.) Multimedia and Network Information Systems, MISSI 2006. PWr Publishing house, Wroclaw (2006)

    Google Scholar 

  180. Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: prospect, methods and challenge. J. Group Decis. Negot. 10(2), 199–215 (2001)

    Article  Google Scholar 

  181. Niazi, M., Hussain, A.: Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics 89(2), 479–499 (2011)

    Article  Google Scholar 

  182. Kielar, P.M., Borrmann, A.: Auton. Agents Multi-Agent Syst. 32, 387 (2018). https://doi.org/10.1007/s10458-018-9383-2

    Article  Google Scholar 

  183. Li, G., Whiteson, S., Knox, W.B., et al.: Auton. Agents Multi-Agent Syst. 32, 1 (2018). https://doi.org/10.1007/s10458-017-9374-8

    Article  Google Scholar 

  184. Gabel, T., Riedmiller, M.: On a successful application of multi-agent reinforcement learning to operations research benchmarks. In: IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, Honolulu, pp. 68–75 (2007)

    Google Scholar 

  185. Doniec, A., Mandiau, R., Piechowiak, S., Espié, S.: A behavioral multi-agent model for road traffic simulation. Eng. Appl. Artif. Intell. 21(8), 1443–1454 (2008)

    Article  Google Scholar 

  186. Lecoutre, Ch., Saïs, L., Tabary, S., Vidal, V.: Reasoning from last conflict(s) in constraint programming. Artif. Intell. 173(18), 1592–1614 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  187. Abbas, J.: Social software use in public libraries. In: Dumova, T., Fiordo, R. (eds.) Handbook of Research on Social Interaction Technologies and Collaboration Software: Concepts and Trends. IGI Global, Hershey, New York (2009)

    Google Scholar 

  188. Uden, L., Eardley, A.: The usability of social software. In: Dumova, T., Fiordo, R. (eds.) Handbook of Research on Social Interaction Technologies and Collaboration Software: Concepts and Trends. IGI Global, Hershey, New York (2009)

    Google Scholar 

  189. Mirkin, B., Shestakov, A.: A note on the effectiveness of the least squares consensus clustering. In: Aleskerov, F., Goldengorin, B., Pardalos, P.M. (eds.) Clusters, Orders, and Trees: Methods and Applications. SOIA, vol. 92, pp. 181–185. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0742-7_11

    Chapter  Google Scholar 

  190. Mercier-Laurent, E.: Knowledge management and risk management. In: Proceedings of Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, pp. 1369–1373 (2016)

    Google Scholar 

  191. Domenach, F., Tayari, A.: Implications of axiomatic consensus properties. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds.) Algorithms from and for Nature and Life. Studies in Classification Data Analysis and Knowledge Organization. Springer, Cham (2013)

    MATH  Google Scholar 

  192. Castano, S., Ferrara, A., Montanelli, S.: Designing crowdsourcing tasks with consensus constraints. In: International Conference on Collaboration Technologies and Systems (CTS), Orlando, FL, pp. 97–103 (2016)

    Google Scholar 

  193. Kozierkiewicz-Hetmańska, A., Pietranik, M.: The knowledge increase estimation framework for ontology integration on the relation level. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 44–53. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67074-4_5

    Chapter  Google Scholar 

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

    Google Scholar 

  195. Condorcet, M.: Essai sur l’application de l’analyse a la probabilite des decisions rendues ala prularite des voix. Chelsea Published, no. 6, New York (1974)

    Google Scholar 

  196. Maleszka, M., Nguyen, N.T.: Integration computing and collective intelligence. Expert Syst. Appl. 42(1), 332–340 (2015)

    Article  Google Scholar 

  197. Barthlemy, J.P.: Dictatorial consensus function on n-trees. Math. Soc. Sci. 25, 59–64 (1992)

    Article  MathSciNet  Google Scholar 

  198. McMorris, F.R., Powers, R.C.: The median function on weak hierarchies. DIMACS Ser. Discret. Math. Theoret. Comput. Sci. 37, 265–269 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  199. Sobieska-Karpińska, J., Hernes, M.: The postulates of consensus determining in financial decision support systems. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Kraków (2013)

    Google Scholar 

  200. Hernes, M., Sobieska-Karpińska, J.: Susceptibility to consensus of conflict situation in intelligent multi-agent decision support system. In: Kubiak, B.F., Korowicki, A. (eds.) Information Management. Gdansk University Press, Gdańsk (2009)

    Google Scholar 

  201. Bytniewski, A., Hernes, M.: Algorithm for determining consensus in a situation of conflict of unstructured knowledge in distributed IT systems supporting management. Ekonometria 4(42), 153–164 (2013). (in Polish)

    Google Scholar 

  202. Hernes, M.: Deriving consensus for term frequency matrix in a cognitive integrated management information system. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS (LNAI), vol. 9329, pp. 503–512. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24069-5_48

    Chapter  Google Scholar 

  203. Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)

    Article  MathSciNet  MATH  Google Scholar 

  204. Maleszka, M., Mianowska, B., Nguyen, N.T.: A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles. Knowl.-Based Syst. 47, 1–13 (2013)

    Article  Google Scholar 

  205. Truong, H.B., Nguyen, N.T.: A multi-attribute and multi-valued model for fuzzy ontology integration on instance level. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012. LNCS (LNAI), vol. 7196, pp. 187–197. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28487-8_19

    Chapter  Google Scholar 

  206. Truong, H.B., Quach, X.H.: An overview of fuzzy ontology integration methods based on consensus theory. In: van Do, T., Thi, H.A.L., Nguyen, N.T. (eds.) Advanced Computational Methods for Knowledge Engineering. AISC, vol. 282, pp. 217–227. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06569-4_16

    Chapter  MATH  Google Scholar 

  207. Nguyen, Q.U., Duong, T.H., Kang, S.: Solving conflict on collaborative knowledge via social networking using consensus choice. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012. LNCS (LNAI), vol. 7653, pp. 21–30. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34630-9_3

    Chapter  Google Scholar 

  208. Jung, J.J., Nguyen, N.T.: Consensus choice for reconciling social collaborations on semantic wikis. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 472–480. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04441-0_41

    Chapter  Google Scholar 

  209. Sobecki, J.: Hybrid adaptation of web-based systems user interfaces. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 505–512. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24688-6_66

    Chapter  Google Scholar 

  210. Jajuga, K.: Managing risk and investment in small business. In: Porada-Rochoń, M., Mifsud, J., Pittella, G. (eds.) Managing a Small Business in the Contemporary Environment, pp. 175–194 (2012)

    Google Scholar 

  211. Korczak, J., Hernes, M., Bac, M.: Performance evaluation of decision-making agents’ in the multi-agent system. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Warszawa, pp. 1177–1184 (2014)

    Google Scholar 

  212. Hernes, M., Chojnacka-Komorowska, A., Matouk, K.: Analysis of text documents in a multi-agent integrated management system. In: Porębska-Miąc, T. (ed.) Systemy Wspomagania Organizacji. Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, Katowice (2015). (in Polish)

    Google Scholar 

  213. Hernes, M., Chojnacka-Komorowska, A., Matouk, K.: External environment scanning using cognitive agents. In: Nguyen, N.T., Papadopoulos, George A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 342–350. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67074-4_33

    Chapter  Google Scholar 

  214. Bartusiak, R., Kajdanowicz, T.: Sentiment analysis based on collaborative data for Polish language. In: Luo, Y. (ed.) CDVE 2015. LNCS, vol. 9320, pp. 216–219. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24132-6_27

    Chapter  Google Scholar 

  215. Piasecki, M.: Polish tagger TaKIPI: rule based construction and optimisation. Task Q. 11(1–2), 151–167 (2007)

    Google Scholar 

  216. Sokołowska, W., Hossa, T., Fabisz, K., Abramowicz, W., Kubaczyk, M.: Sentiment analysis as a source of gaining competitive advantage on the electricity markets. J. Electron. Sci. Technol. 13(3), 229–236 (2015)

    Google Scholar 

  217. Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson, London (2009)

    Google Scholar 

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    Marcin Hernes

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    Ngoc Thanh Nguyen

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  3. Wrocław University of Economics, Wrocław, Poland

    Marcin Hernes

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Hernes, M. (2019). Consensus Theory for Cognitive Agents’ Unstructured Knowledge Conflicts Resolving in Management Information Systems. In: Nguyen, N., Kowalczyk, R., Hernes, M. (eds) Transactions on Computational Collective Intelligence XXXII. Lecture Notes in Computer Science(), vol 11370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58611-2_1

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