Multi-Agent Strategic Modeling in a Specific Environment

  • Matjaz Gams
  • Andraz Bezek


Multi-agent modeling in ambient intelligence (AmI) is concerned with the following task [19]: How can external observations of multi-agent systems in the ambient be used to analyze, model, and direct agent behavior? The main purpose is to obtain knowledge about acts in the environment thus enabling proper actions of the AmI systems [1]. Analysis of such systems must thus capture complex world state representation and asynchronous agent activities. Instead of studying basic numerical data, researchers often use more complex data structures, such as rules and decision trees. Some methods are extremely useful when characterizing state space, but lack the ability to clearly represent temporal state changes occurred by agent actions. To comprehend simultaneous agent actions and complex changes of state space, most often a combination of graphical and symbolical representation performs better in terms of human understanding and performance.


Multiagent System Rule Inducer Ambient Intelligence Action Instance Action Description 
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.


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  1. [1]
    J. C. Augusto: Ambient Intelligence: Basic Concepts and Applications. In Selected Papers from ICSOFT’06. Springer Verlag. 2008.Google Scholar
  2. [2]
    A. Bezek: Automatic Modeling of Multiagent systems. Phd. Thesis, Faculty of Computer and Information Science, Ljubljana, 2007.Google Scholar
  3. [3]
    A. Bezek: Modeling Multiagent Games Using Action Graphs. Proceedings of Modeling Other Agents from Observations (MOO 2004), 2004.Google Scholar
  4. [4]
    A. Bezek: Discovering Strategic Multi-Agent Behavior in a Robotic Soccer Domain. Proceedings of AAMAS 05, 2005.Google Scholar
  5. [5]
    A. Bezek, M. Gams, and I. Bratko, Multi-agent strategic modeling in a robotic soccer domain. In Proc. AAMAS, 2006, pp.457-464Google Scholar
  6. [6]
    J. Calmet, A. Daemi: From Entropy to Ontology. Proceedings of Fourth International Symposium From Agent Theory to Agent Implementation, 2004.Google Scholar
  7. [7]
    W. W. Cohen and Y. Singer: A simple, fast, and effective rule learner. Proceedings of the sixteenth national conference on Artificial intelligence, pp.: 335 - 342, Orlando, United States, 1999.Google Scholar
  8. [8]
    M. Gams: Weak intelligence : through the principle and paradox of multiple knowledge, (Advances in computation, vol. 6). Huntington: Nova Science, 2001.Google Scholar
  9. [9]
    S. Hirano in S. Tsumoto: Finding Interesting Pass Patterns from Soccer Game Records. The Eighth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-2004), 2004.Google Scholar
  10. [10]
    G. Kaminka in D. Avrahami: Symbolic Behavior-Recognition. Proceedings of Modeling Other Agents from Observations (MOO 2004), 2004.Google Scholar
  11. [11]
    G. Kaminka, M. Fidanboylu, A. Chang, in M. Veloso: Learning the sequential coordinated behavior of teams from observations. Proceedings of the RoboCup-2002 Symposium, 2002.Google Scholar
  12. [12]
    D. Koller and B. Milch: Multi-Agent Influence Diagrams for Representing and Solving Games. Games and Economic Behavior, 45(1), pp. 181-221, 2003.zbMATHCrossRefMathSciNetGoogle Scholar
  13. [13]
    G. Kuhlmann, P. Stone, and J. Lallinger: The UT Austin Villa 2003 Champion Simulator Coach: A Machine Learning Approach. In Daniele Nardi, et al., editors, RoboCup-2004: Robot Soccer World Cup VIII, Springer Verlag, Berlin, 2005.Google Scholar
  14. [14]
    R. Nair, M. Tambe in S. Marsella: Role allocation and reallocation in multiagent teams: Towards a practical analysis. Proceedings of the second International Joint conference on agents and multiagent systems, 2003.Google Scholar
  15. [15]
    S. S. Intille: Visual Recognition of Multi-Agent Action. PhD Thesis, MIT, 1999.Google Scholar
  16. [16]
    I. Noda et. al: Overview of RoboCup-97. In Hiroaki Kitano, editor, RoboCup-97: Robot Soccer World Cup I, pp. 20-41. Springer Verlag, 1997.Google Scholar
  17. [17]
    T. Raines, M. Tambe, and S. Marsella: Towards automated team analysis: a machine learning approach. Third international RoboCup competitions and workshop, 1999.Google Scholar
  18. [18]
    RoboCup 2004: RoboCup Simulation League. RoboCup04 world cup game repository,, 2004.
  19. [19]
    T. Steffens et. al: RoboCup Special Interest Group on Multi-Agent Modelling., 2001.
  20. [20]
    G. Sukthankar and K. Sycara: A cost minimization approach to human behavior recognition. Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, The Netherlands, ACM Press, 2005.Google Scholar
  21. [21]
    D. Suryadi and P. J. Gmytrasiewicz: Learning models of other agents using influence diagrams. Proceedings of the Int. Conference on User Modeling, pp. 223-232, 1999.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Intelligent SystemsJozef Stefan InstituteSlovenia

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