Modeling and Simulation of Tests for Agents

  • Martina Gierke
  • Jan Himmelspach
  • Mathias Röhl
  • Adelinde M. Uhrmacher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4196)


Software systems that are intended to work autonomously in complex, dynamic environments should undergo extensive testing. Model-based testing advocates the use of purpose-driven abstractions for designing appropriate tests. The type of the software, the objective of testing, and the stage of the development process influence the suitability of tests. Simulation techniques based on formal modeling concepts can make these abstractions explicit and operational. A simulation model is presented that facilitates testing of autonomous software within dynamic environments in a flexible manner. The approach is illustrated based on the application Autominder.


Virtual Environment Temporal Abstraction State Transition Function Test Case Selection Test Architecture 
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.
    Dam, K.H., Winikoff, M.: Comparing agent-oriented methodologies. In: Proceedings of the Fifth International Bi-Conference Workshop on Agent-Oriented Information Systems, Melbourne (2003)Google Scholar
  2. 2.
    Hilaire, V., Koukam, A., Gruer, P., Müller, J.P.: Formal specification and prototyping of multi-agent systems. In: Omicini, A., Tolksdorf, R., Zambonelli, F. (eds.) ESAW 2000. LNCS, vol. 1972, pp. 114–127. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Beizer, B.: Software Testing Techniques, 2nd edn., Van Nostrand Reinhold, New York (1990)Google Scholar
  4. 4.
    Bashir, I., Goel, A.L.: Testing Object-Oriented Software: Life Cycle Solutions. Springer, Heidelberg (2000)Google Scholar
  5. 5.
    Prenninger, W., Pretschner, A.: Abstractions for model-based testing. In: Proc. Test and Analysis of Component-based Systems (TACoS 2004), Barcelona (2004)Google Scholar
  6. 6.
    Hahn, G., Philipps, J., Pretschner, A., Stauner, T.: Prototype-based tests for hybrid reactive systems. In: Proc. 14th IEEE Intl. Workshop on Rapid System Prototyping (RSP 2003), pp. 78–85. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  7. 7.
    Kopetz, H.: Software engineering for real-time: a roadmap. In: ICSE - Future of SE Track, pp. 201–211. ACM Press, New York (2000)CrossRefGoogle Scholar
  8. 8.
    Himmelspach, J., Uhrmacher, A.M.: A component-based simulation layer for JAMES. In: Proc. of the 18th Workshop on Parallel and Distributed Simulation (PADS), May 16-19, Kufstein, Austria, pp. 115–122 (2004)Google Scholar
  9. 9.
    Zeigler, B.P., Praehofer, H., Kim, T.G.: Theory of Modeling and Simulation, 2nd edn. Academic Press, London (2000)Google Scholar
  10. 10.
    Uhrmacher, A.M.: Dynamic Structures in Modeling and Simulation - a Reflective Approach. ACM Transactions on Modeling and Simulation 11(2), 206–232 (2001)CrossRefGoogle Scholar
  11. 11.
    Zeigler, B.P.: Multifacetted Modelling and Discrete Event Simulation. Academic Press, London (1984)MATHGoogle Scholar
  12. 12.
    Röhl, M., Uhrmacher, A.M.: Controlled experimentation with agents – models and implementations. In: Gleizes, M.-P., Omicini, A., Zambonelli, F. (eds.) ESAW 2004. LNCS, vol. 3451, pp. 292–304. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Pollack, M.E., Brown, L., Colbry, D., McCarthy, C.E., Orosz, C., Peintner, B., Ramakrishnan, S., Tsamardinos, I.: Autominder: An Intelligent Cognitive Orthotic System for People with Memory Impairment. Robotics and Autonomous Systems 44, 273–282 (2003)CrossRefGoogle Scholar
  14. 14.
    Gierke, M.: Coupling Autominder and James. Master’s thesis, University of Rostock (2004)Google Scholar
  15. 15.
    Gierke, M., Uhrmacher, A.M.: Modeling Elderly Behavior for Simulation-based Testing of Agent Software. In: Conceptual Modeling and Simulation CSM 2005 (2005)Google Scholar
  16. 16.
    Uhrmacher, A.M., Röhl, M., Kullick, B.: The role of reflection in simulating and testing agents: An exploration based on the simulation system james. Applied Artificial Intelligence 16(9-10), 795–811 (2002)CrossRefGoogle Scholar
  17. 17.
    Rudary, M., Singh, S., Pollack, M.E.: Adaptive Cognitive Orthotics: Combining Reinforcement Learning and Constraint-Based Temporal Reasoning. In: 21st International Conference on Machine Learning (2004)Google Scholar
  18. 18.
    Sifakis, J., Tripakis, S., Yovine, S.: Building models of real-time systems from application software. Proceedings of the IEEE 91(1), 100–111 (2003)CrossRefGoogle Scholar
  19. 19.
    MathWorks: Simulink (2005),
  20. 20.
    Pollack, M.E.: Planning in dynamic environments: The DIPART system. In: Tate, A. (ed.) Advanced Planning Technology: Technological Achievements of the ARPA/Rome Laboratory Planning Initiative, pp. 218–225. AAAI Press, Menlo Park (1996)Google Scholar
  21. 21.
    Anderson, S.D.: Simulation of multiple time-pressured agents. In: Proc. of the Wintersimulation Conference, WSC 1997, Atlanta (1997)Google Scholar
  22. 22.
    Kitano, H., Tadokoro, S.: RoboCup Rescue: A grand challenge for multiagent and intelligent systems. AI Magazine 22(1), 39–52 (2001)Google Scholar
  23. 23.
    Wiles, A.: ETSI testing activities and the use of TTCN-3. In: Reed, R., Reed, J. (eds.) SDL 2001. LNCS, vol. 2078, pp. 123–128. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  24. 24.
    Schattenberg, B., Uhrmacher, A.M.: Planning agents in James. Proceedings of the IEEE 89(2), 158–173 (2001)CrossRefGoogle Scholar
  25. 25.
    DIANE-Projekt: Dienste in Ad-Hoc-Netzen (2005),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Martina Gierke
    • 1
  • Jan Himmelspach
    • 1
  • Mathias Röhl
    • 1
  • Adelinde M. Uhrmacher
    • 1
  1. 1.Institute of Computer ScienceUniversity of RostockRostockGermany

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