Advertisement

Extraction of Agent Groups with Similar Behaviour Based on Agent Profiles

  • Kateřina Slaninová
  • Jan Martinovič
  • Roman Šperka
  • Pavla Dráždilová
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8104)

Abstract

This paper deals with the log files suitable to extract valuable information about agents and their behaviour from agent-based simulation in a model of virtual company. Such information, presented in a transparent way, can be used as a support for simulation verification to achieve the suitable design of the proposed system. Hence, based on the similar behaviour (represented by extracted sequences) of agents, we are able to construct models which explain certain aspects of agent behaviour. Moreover, we can extract agent profiles based on behaviour and find latent ties between different agent groups with similar behaviours. The paper extends the results of our previous works about sequence extraction and comparison. The approach for agent network construction based on agent profiles is described. Two different methods were used for construction of agent network. One method uses cosine similarity and graph partitioning and the second self organization maps and Euclidean similarity for agent relations. Each of these methods has its advantages and disadvantages which are summarized in the paper and presented in the form of the visualization of relations between agents.

Keywords

Agent Profile Log Analysis Agent Behaviour 

References

  1. 1.
    Barnett, M.W.: Modeling & simulation in business process management. Gensym Corporation (2003)Google Scholar
  2. 2.
    Wooldridge, M.: An Introduction to MultiAgent Systems, 2nd edn. Wiley Publishing (2009)Google Scholar
  3. 3.
    Macal, C.M., North, M.J.: Tutorial on agent-based modeling and simulation. In: Proceedings of the 37th Conference on Winter Simulation, WSC 2005, pp. 2–15. Winter Simulation Conference (2005)Google Scholar
  4. 4.
    van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering social networks from event logs. Comput. Supported Coop. Work 14(6), 549–593 (2005)CrossRefGoogle Scholar
  5. 5.
    van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: a survey of issues and approaches. Data Knowl. Eng. 47(2), 237–267 (2003)CrossRefGoogle Scholar
  6. 6.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, 1st edn. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  7. 7.
    Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer Series in Information Sciences, vol. 8. Springer, Heidelberg (1984)zbMATHGoogle Scholar
  8. 8.
    Vojáček, L., Martinovič, J., Slaninová, K., Dráždilová, P., Dvorský, J.: Combined method for effective clustering based on parallel som and spectral clustering. In: Snášel, V., Pokorný, J., Richta, K. (eds.) DATESO 2011, VŠB - TU Ostrava, pp. 120–131 (2011)Google Scholar
  9. 9.
    Kannan, R., Vempala, S., Vetta, A.: On clusterings: Good, bad and spectral. J. ACM 51(3), 497–515 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Ding, C.H.Q., He, X., Zha, H., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: ICDM 2001: Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 107–114. IEEE Computer Society, Washington, DC (2001)CrossRefGoogle Scholar
  11. 11.
    Dráždilová, P., Martinovič, J., Slaninová, K.: Spectral clustering: Left-right-oscillate algorithm for detecting communities. In: ADBIS Workshops, pp. 285–294 (2012)Google Scholar
  12. 12.
    De Snoo, D.: Modeling planning processes with talmod. Master’s thesis, University of Groningen (2005)Google Scholar
  13. 13.
    Jennings, N., Faratin, P., Norman, T., O’Brien, P., Odgers, B.: Autonomous agents for business process management. Int. Journal of Applied Artificial Intelligence 14, 145–189 (2000)CrossRefGoogle Scholar
  14. 14.
    Macal, C., North, J.: Tutorial on agent-based modeling and simulation. In: Proceedings: 2005 Winter Simulation Conference (2005)Google Scholar
  15. 15.
    Scheer, A.-W., Nüttgens, M.: ARIS architecture and reference models for business process management. In: van der Aalst, W.M.P., Desel, J., Oberweis, A. (eds.) Business Process Management. LNCS, vol. 1806, pp. 376–389. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  16. 16.
    Spišák, M., Šperka, R.: Financial market simulation based on intelligent agents - case study. Journal of Applied Economic Sciences VI(17), 249–256 (2011)Google Scholar
  17. 17.
    Newman, M.E.J.: Networks: An Introduction. Oxford University Press (2010)Google Scholar
  18. 18.
    Slaninová, K., Martinovič, J., Dráždilová, P., Vymětal, D., Šperka, R.: Analysis of agents’ behavior in multiagent system. In: 24th European Modeling and Simulation Symposium, EMSS 2012, pp. 169–175 (2012)Google Scholar
  19. 19.
    Deza, M.M., Deza, E.: Dictionary of Distances. Elsevier Science, Amsterdam (2006)zbMATHGoogle Scholar
  20. 20.
    Elmore, K.L., Richman, M.B.: Euclidean distance as a similarity metric for principal component analysis. Monthly Weather Review 129, 540 (2001)CrossRefGoogle Scholar
  21. 21.
    Pampalk, E., Rauber, A., Merkl, D.: Using smoothed data histograms for cluster visualization in self-organizing maps. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 871–876. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Kateřina Slaninová
    • 1
  • Jan Martinovič
    • 2
  • Roman Šperka
    • 1
  • Pavla Dráždilová
    • 2
  1. 1.School of Business Administration in KarvináSilesian University in OpavaKarvináCzech Republic
  2. 2.Faculty of Electrical EngineeringVŠB Technical University in OstravaOstravaCzech Republic

Personalised recommendations