Dynamic Analysis of Agents’ Behaviour – Combining ALife, Visualization and AI

  • Pavel Nahodil
  • Pavel Slavík
  • David Rehor
  • David Kadlecek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3071)


The analysis of an agent or agent communities combining advanced methods of visualization with traditional AI techniques is presented in this paper. However this approach can be used for arbitrary Multi-Agent System (MAS), it was primarily developed to analyse systems falling into Artificial Life domain. Traditional methods are becoming insufficient as MAS are becoming more complex and therefore novel approaches are needed. Our approach builds upon various techniques to deliver means for assessment on multiple levels ranging from single agent to overall properties of an agent community. Our visualization tools suite utilizes novel visualization methods together with traditional AI techniques such as sensitivity analysis and clustering. Among others it offers visualization of many agent’s parameters along time, correspondence between current/previous states (of an agent community), resulting behaviour, grouping of agents based on dominant properties etc. This transparent approach emphasizes MAS dynamics through automatic discovery of its tendency. Agent position inside virtual environment together with overview over the whole time interval adds strong contextual information to analysis. Position in our understanding is not limited to geometrical meaning, but covers also the space of dynamically changing constraints for action selection. A simulated artificial life environment with intelligent agents has been used as a test bed. We have selected this particular domain because our long-term goal is to model life as it could be so as to understand life, as we know it.


Agent Community Visualization Method Artificial Life Agent Position Intelligent User Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Szekely, P., Rogers, C.M.: Interfaces for Understanding Multi-Agent Behavior. In: Proc. ACM Conf. on Intelligent User Interfaces, Santa Fe New Mexico USA, pp. 161–166 (2001)Google Scholar
  2. 2.
    Beneš, B., Espinosa, E.: Modeling Virtual Ecosystems with Proactive Guidance of Agents. In: IEEE Computer Animation and Social Agents, New Bruncwick USA (2003) (to appear)Google Scholar
  3. 3.
    Allbeck, J., Badler, N.: Toward Representing Agent Behaviors Modified by Personality and Emotion. In: Workshop on Embodied Conversational Agents, Bologna, Italy (2002)Google Scholar
  4. 4.
    Schroeder, M., Noy, P.: Multiagent Visualisation Based on Multivariate Data. In: Proc. 5th International Conf. on Autonomous Agents, Montreal Canada, pp. 85–91 (2001)Google Scholar
  5. 5.
    Chatterjee, S., Hadi, A.S.: Sensitivity Analysis in Linear Regression. John Wiley & Sons, New York (1988)MATHCrossRefGoogle Scholar
  6. 6.
    Inselberg, A., Dimsdale, B.: Parallel Coordinates: A Tool for Visualizing Multivariate Relations. Plenum Publishing Corporation, New York (1991)Google Scholar
  7. 7.
    Havre, S., Hetzler, E., et al.: ThemeRiver: Visualizing Thematic Changes in Large Document Collections. IEEE Trans. on Visualization and Computer Graphics 8(1) (2002)Google Scholar
  8. 8.
    Nahodil, P., Kadlecek, D., Rehor, D., Slavík, P.: Transparent Visualization of Multi-Agent Systems. In: Proc. 4th International Carpathian Control Conference, High Tatras Slovakia, pp. 723–727 (2003)Google Scholar
  9. 9.
    Rehor, D., Kadlecek, D., Slavík, P., Nahodil, P.: VAT – An Approach to Multi-Agent System Vizualization. In: IASTED International Conference on Visualization, Imaging and Image Processing, Benalmadena Spain, pp. 849–854 (2003)Google Scholar
  10. 10.
    Kadlecek, D., Nahodil, P.: New Hybrid Architecture in Artificial Life Simulation. In: Kelemen, J., Sosík, P. (eds.) ECAL 2001. LNCS (LNAI), vol. 2159, pp. 143–146. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    McFarland, D., Bosser, U.: Intelligent Behavior in Animals and Robots. MIT Press, Cambridge (1993)Google Scholar
  12. 12.
    Ferber, J.: Multi Agent Systems. An Introduction to Distributed Artificial Intelligence. Addison-Wesley, Reading (1999)Google Scholar
  13. 13.
    Kadlecek, D., Rehor, D., Nahodil, P., Slavík, P.: Analysis of Virtual Agent Communities by Means of AI Techniques and Visualization. In: Intelligent Virtual Agents, pp. 274–282. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Slavík, P., Míkovec, Z., Hrdlicka, F.: Special Problems of Visualization in a Specific Environment. In: East-West-Vision, pp. 269–270. Österreichische Computer Gesellschaft, Wien (2002)Google Scholar
  15. 15.
    Nahodil, P., Petrus, M.: Behaviour Co-ordination in Multi-Robot Group. In: Proceedings of the International IASTED Conference MIC, February 18-21, pp. 464–468. ACTA Press, Innsbruck (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Pavel Nahodil
    • 1
  • Pavel Slavík
    • 2
  • David Rehor
    • 2
  • David Kadlecek
    • 1
  1. 1.Dept. of CyberneticsFEE, Czech Technical University in PraguePrague 2Czech Republic
  2. 2.Dept. of Computer Science and EngineeringFEE, Czech Technical University in PraguePrague 2Czech Republic

Personalised recommendations