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Automated Interpretation of Agent Behaviour

  • D. N. Lam
  • K. S. Barber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3529)

Abstract

Software comprehension, which is essential for debugging and maintaining software systems, has lacked attention in the agent community. Comprehension has been a manual process, involving the analysis and interpretation of log files that record agent behaviour in the implemented system. This paper describes an approach and tool to automate creating interpretations of agent behaviour from observations of the implementation execution, thus helping users (i.e. designers, developers, and end-users) to understand the motivations of agent actions. By explicitly modelling the user’s comprehension of the implemented system as background knowledge for the tool, feedback can be provided as to whether the user’s comprehension accurately represents the implementation’s behaviour and, if not, how it can be corrected. Additionally, with the aid of the Tracer Tool, many of the manual tasks are automated, such as verifying that agents are behaving as expected, identifying unexpected behaviour and generating explanations for any particular observation.

Keywords

Background Knowledge MultiAgent System Reverse Engineering Agent Software Defense Advance Research Project Agency 
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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References

  1. 1.
    Stroulia, E., Syst, T.: Dynamic Analysis for Reverse Engineering and Program Understanding. ACM SIGAPP Applied Computing Review 10(1), 8–17 (2002)CrossRefGoogle Scholar
  2. 2.
    Agrawal, A., Du, M., McCollum, C., Syst, T., Wong, K., Yu, P., Mller, H.: Rigi - An End-User Programmable Tool for Identifying Reusable Components. In: 5th International Conference on Software Reuse, Victoria, British Columbia (1998)Google Scholar
  3. 3.
    Finnigan, P.J., Holt, R.C., Kalas, I., Kerr, S., Kontogiannis, K., Meller, H.A., Mylopoulos, J., Perelgut, S.G., Stanley, M., Wong, K.: The Software Bookshelf. IBM Systems Journal 36(4), 564–593 (1997)CrossRefGoogle Scholar
  4. 4.
    Wooldridge, M.: An Introduction to MultiAgent Systems. John Wiley and Sons, Chichester (2002)Google Scholar
  5. 5.
    Koskimies, K., Mnnist, T., Syst, T., Tuomi, J.: Automated Support for Modeling OO Software. IEEE Software 15(1), 87–94 (1998)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Bruening, D., Devabhaktuni, S., Amarasinghe, S.: Softspec: Software-based Speculative Parallelism. In: 3rd ACM Workshop on Feedback-Directed and Dynamic Optimization, Montery, California. ACM Press, New York (2000)Google Scholar
  8. 8.
    Kullbach, B., Winter, A.: Querying as an Enabling Technology in Software Reengineering. In: Nesi, P., Verhoef, C. (eds.) 3rd European Conf. on Software Maintenance and Reengineering, pp. 42–50. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  9. 9.
    Jennings, N.R.: On Agent-based Software Engineering. Artificial Intelligence 117, 277–296 (2000)MATHCrossRefGoogle Scholar
  10. 10.
    Lam, D.N., Barber, K.S.: Debugging Agent Behavior in an Implemented Agent System. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) PROMAS 2004. LNCS, vol. 3346, pp. 104–125. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Lam, D.N., Bosse, T., Barber, K.S.: Automated Analysis and Verification of Agent Behavior. In: 5th International Conference on Autonomous Agents and Multiagent Systems, Hakodate, Japan, pp. 1317–1319 (2006)Google Scholar
  12. 12.
    Lam, D.N., Barber, K.S.: Comprehending Agent Software. In: 4th International Joint Conference on Autonomous Agents and Multi-Agent Systems, Utrecht, Netherlands (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • D. N. Lam
    • 1
  • K. S. Barber
    • 1
  1. 1.The Laboratory for Intelligent Processes and SystemsThe University of Texas at Austin 

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