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Steps Towards Making Contextualized Decisions: How to Do What You Can, with What You Have, Where You Are

  • Oana Bucur
  • Philippe Beaune
  • Olivier Boissier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3946)

Abstract

Applications need facilities for recognizing and adapting to context in order to provide useful and user-centered results. There are several problems to be addressed when building context-aware applications, two of which being how to defineand manage all available contextual information and how to distinguishrelevant from non-relevant context for a given task. In this paper, we focus on the second problem and propose a context definition and model for a context-aware agent. We exploit this model to build agents that learn to select relevant context and to use it to make decisions.

Keywords

Decision Module Domain Ontology Relevant Context Context Manager Context Attribute 
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.
    Brézillon, P.: Context dynamic and explanation in contextual graphs. In: Blackburn, P., Ghidini, C., Turner, R.M., Giunchiglia, F. (eds.) CONTEXT 2003. LNCS (LNAI), vol. 2680, pp. 94–106. Springer, Heidelberg (2003), http://link.springer.de/link/service/series/0558/tocs/t2680.htm CrossRefGoogle Scholar
  2. 2.
    Chen, H., Finin, T., Anupam, J.: An Ontology for Context-Aware Pervasive Computing Environments. The Knowledge Engineering Review 18(3), 197–207 (2003)CrossRefGoogle Scholar
  3. 3.
    CLIPS (C Language Integrated Production System), http://www.ghg.net/clips/CLIPS.html (last visited: October 10, 2005)
  4. 4.
    Coutaz, J., Ray, G.: Foundations for a theory of contextors. In: Proc. CADUI 2002, pp. 283–302. ACM Publication, New York (2002)Google Scholar
  5. 5.
    Coutaz, J., et al.: Context is key. The disappearing computer, Special Issue: The disappearing computer 48(3), 49–53 (2005)Google Scholar
  6. 6.
    Data Mining II – CBA, http://www.comp.nus.edu.sg/~dm2/ (last visited: October 10, 2005)
  7. 7.
    Dey, A., Abowd, G.: Towards a better understanding of Context and Context- Awareness. GVU Technical Report GIT-GVU-00-18, GIT (1999)Google Scholar
  8. 8.
    Edmonds, B.: Learning and exploiting context in agents. In: Proceedings of The First International Joint Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2002, Bologna, Italy, July 15-19, pp. 1231–1238 (2002)Google Scholar
  9. 9.
    Gonzalez, A., Ahlers, R.: Context based representation of intelligent behavior in training simulations. Transactions of the Society for Computer Simulation International 15(4), 153–166 (1999)Google Scholar
  10. 10.
    Henricksen, K., Indulska, J., Rakotonirainy, A.: Modeling Context Information in Pervasive Computing Systems. In: Mattern, F., Naghshineh, M. (eds.) PERVASIVE 2002. LNCS, vol. 2414, pp. 167–180. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    JADE (Java Agent Development framework), http://jade.cselt.it/ (last visited: October 10, 2005)
  12. 12.
    Jena Semantic Web Framework, http://jena.sourceforge.net/ (last visited: October 10, 2005)
  13. 13.
    Jess, http://herzberg.ca.sandia.gov/jess/index.shtml (last visited: October 10, 2005)
  14. 14.
    Lashkari, Y., Metral, M., Maes, P.: Collaborative Interface Agents. In: Proc. of the Third International Conference on Information and Knowledge Management CIKM 1994. ACM Press, New York (1994)Google Scholar
  15. 15.
    Lin, S., Hsu, J.Y.: Learning User’s Scheduling Criteria in a Personal Calendar Agent. In: Proc. of TAAI 2000, Taipei (2000)Google Scholar
  16. 16.
    Maes, P.: Agents that reduce work and information overload. Communications of the ACM 37(7) (July 1994)Google Scholar
  17. 17.
    Matsatsinis, N.F., Moratis, P., Psomatakis, V., Spanoudakis, N.: An Intelligent Software Agent Framework for Decision Support Systems Development. In: ESIT 1999 (European Symposium on Intelligent Techniques) (1999)Google Scholar
  18. 18.
    Mitchell, T., Caruana, R., Freitag, D., McDermott, J., Zabowski, D.: Experience with a learning personal assistant. Communications of the ACM (1994)Google Scholar
  19. 19.
    OWL, http://www.w3.org/2004/OWL/ (last visited: October 10, 2005)
  20. 20.
    Ossowski, S., Fernandez, A., Serrano, J.M., Hernandez, J.Z., Garcia-Serrano, A.M., Perez-dela- Cruz, J.L., Belmonte, M.V., Maseda, J.M.: Designing Multiagent Decision Support System. The Case of Transportation Management. In: ACM/AAMAS 2004, New York, pp. 1470–1471 (2004)Google Scholar
  21. 21.
    Persson, P.: Social Ubiquitous computing. In: Position paper to the workshop on ‘Building the Ubiquitous Computing User Experience’ at ACM/SIGCHI 2001, Seattle (2001)Google Scholar
  22. 22.
    Protégé 2000, http://protege.stanford.edu/ (last visited: October 10, 2005)
  23. 23.
    Ranganathan, A., Campbell, R.: An infrastructure for context-awareness based on first order logic. Personal and Ubiquitous Computing 7(6), 353–364 (2003)CrossRefGoogle Scholar
  24. 24.
    Ryan, N.: ConteXtML: Exchanging contextual information between a Mobile Client and the FieldNote Server, http://www.cs.kent.ac.uk/projects/mobicomp/fnc/ConteXtML.html (last visited: October 10, 2005)
  25. 25.
    Scerri, P., Pynadath, D., Tambe, M.: Why the elf acted autonomously: Towards a theory of adjustable autonomy. In: First Autonomous Agents and Multi-agent Systems Conference (AAMAS 2002), pp. 857–964 (2002)Google Scholar
  26. 26.
    Sen, S., Durfee, E.H.: On the design of an adaptive meeting scheduler. In: Proc. of the Tenth IEEE Conference on AI Applications, pp. 40–46 (1994)Google Scholar
  27. 27.
    Sian, S.S.: Adaptation Based on Cooperative Learning in Multi-Agent Systems. In: Demazeau, Y., Muller, J.P. (eds.) Descentralized AI, pp. 257–272 (1991)Google Scholar
  28. 28.
    Gu, T., Xiao Hang, W., Hung, K.P., Da Quing, Z.: An Ontology-based Context Model in Intelligent Environments. In: Proc. of Communication Networks and Distributed Systems Modeling and Simulation Conf. (2004)Google Scholar
  29. 29.
    Turney, P.: The identification of Context-Sensitive Features: A Formal Definition of context for Concept Learning. In: 13th International Conference on Machine Learning (ICML 1996), Workshop on Learning in Context-Sensitive Domains, pp. 53–59 (1996)Google Scholar
  30. 30.
    Turner, R.: Context-Mediated Behaviour for Intelligent Agents. International Journal of Human-Computer Studies 48(3), 307–330 (1998)CrossRefGoogle Scholar
  31. 31.
    Sian, S.S.: Adaptation Based on Cooperative Learning in Multi-Agent Systems. In: Demazeau, Y., Muller, J.P. (eds.) Descentralized AI, pp. 257–272 (1991)Google Scholar
  32. 32.
    Willmott, S., Calisti, M., Rollon, E.: Challenges in Large-Scale Open Agent Mediated Economie. In: Proc. of Workshop on Agent Mediated Electronic Commerce AAMAS 2002 (July 2002)Google Scholar
  33. 33.
    Weiss, G., Dillenbourg, P.: What is “multi” in multi-agent learning? In: Dillenbourg, P. (ed.) Collaborative-learning: Cognitive, and computational approaches, pp. 64–80 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oana Bucur
    • 1
  • Philippe Beaune
    • 1
  • Olivier Boissier
    • 1
  1. 1.Centre G2I/SMA, Ecole NS des Mines de Saint-EtienneSaint-EtienneFrance

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