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A New Distance Function for Consensus Determination in Decision Support Systems

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Computational Collective Intelligence (ICCCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11056))

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Abstract

Consensus methods are used mainly to solve conflicts of knowledge in decision support systems. Generally speaking, conflicts of knowledge arise from the fact that system nodes (for example, agents, experts) may present various decisions or solutions to the user. This may be due to the use of various methods of decision support or different information sources by agents/experts. If there is a conflict of knowledge in the system and they are not automatically resolving the system cannot generate the final decision, and hence - the decision maker will not receive hints from the system. The use of consensus methods eliminates this problem, because they enable to solve conflicts of knowledge in near real time. At the same time they guarantee the achievement of a good compromise. However, the effective determination of consensus depends, among other, on the correct definition of the distance function.

The aim of this paper is to develop a new distance function between the decisions generated by expert of agents in decision support systems.

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References

  1. MetaTrader5. https://www.metatrader5.com

  2. Plus500. https://www.plus500.com/

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

    Google Scholar 

  4. 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. 1119–1126 (2013)

    Google Scholar 

  5. 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, Oficyna Wydawnicza PWr, Wrocław (2006)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. Hernes, M., Sobieska-Karpińska, J.: Application of the consensus method in a multi-agent financial decision support system. Inf. Syst. e-Bus. Manag. 14(1), 167 (2016)

    Article  Google Scholar 

  8. Nguyen, N.T.: Using consensus methodology in processing inconsistency of knowledge. In: Last, M., et al. (eds.) Advances in Web Intelligence and Data Mining. SCI, vol. 23, pp. 161–170. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-33880-2_17

    Chapter  Google Scholar 

  9. Davenport, T.H., Paul, B., Bean, R.: How ‘Big Data’ is different. MIT Sloan Manag. Rev. 54(1), 21–25 (2012)

    Google Scholar 

  10. Zhang, Z.: Social software for customer knowledge management. In: Dumova, T., Fiordo, R. (eds.) Handbook of Research on Social Interaction Technologies and Collaboration Software: Concepts and Trends. IGI Global, Hershey (2009)

    Google Scholar 

  11. Kozierkiewicz-Hetmańska, A., Pietranik, M., Hnatkowska, B.: The knowledge increase estimation framework for ontology integration on the instance level. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS (LNAI), vol. 10191, pp. 3–12. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54472-4_1

    Chapter  Google Scholar 

  12. Wu, Z.B., Xu, J.P.: Consensus reaching models of linguistic preference relations based on distance functions. Soft. Comput. 16, 577–589 (2012)

    Article  Google Scholar 

  13. Meskanen, T., Nurmi, H.: Distance from Consensus: a theme and variations. In: Simeone, B., Pukelsheim, F. (eds.) Mathematics and Democracy. Studies in Choice and Welfare. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-35605-3_9

    Chapter  Google Scholar 

  14. Hernes, M., Sobieska-Karpińska, J.: Consensus determining algorithm for supply chain management systems. Inf. Syst. Manag. 3(1), pp. 27–39 (2014)

    Google Scholar 

  15. Song, J., Gao, Y., Wang, H., An, B.: Measuring the distance between finite Markov decision processes. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, pp. 468–476 (2016)

    Google Scholar 

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

    Google Scholar 

  17. Danilowicz, C., Nguyen, N.T.: Consensus methods for solving inconsistency of replicated data in distributed systems. Distrib. Parallel Databases 14(1), 53–69 (2003)

    Article  Google Scholar 

  18. Dyreson, C.E., Soo, M., Snodgrass, R.T.: The data model for time. In: Snodgrass, R.T. (ed.) The SQL Temporal Query Language. Kluwer Academic Publish, Hingham (1995)

    Google Scholar 

  19. Jajuga, K., Walesiak, M., Bak, A.: On the general distance measure. In: Schwaiger, M., Opitz, O. (eds.) Exploratory Data Analysis in Empirical Research Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-642-55721-7_12

    Chapter  Google Scholar 

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Correspondence to Marcin Hernes .

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Hernes, M., Sobieska-Karpińska, J., Kozierkiewicz, A., Pietranik, M. (2018). A New Distance Function for Consensus Determination in Decision Support Systems. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11056. Springer, Cham. https://doi.org/10.1007/978-3-319-98446-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-98446-9_15

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-98446-9

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