Définition et représentation du contexte pour des agents sensibles au contexte

ABSTRACT

Developing context-aware applications needs facilities for recognizing and representing context, reasoning on it and adapting to it accordingly. In what concerns context representation, the newest and most challenging representation is the ontological one. The problem is that current ontologies for context are quite simplistic and do not provide a standard for representing complex context attributes. In this paper, we propose a context definition and representation used to construct a context-based agent architecture. The representation we propose combines the generality provided by ontologies with the complexity inspired by the object oriented models. The goal of the proposed representation is to support the deployment of context-aware agents able to learn how to recognize and adapt to the context of their decisions.

REFERENCES

• 1: Brezillon, P. - "Context Dynamic and Explanation in Contextual Graphs", CONTEXT-03, p. 94--106, 2003.
• 2: Bucur O, Boissier O, Beaune P - "A Context-Based Agent Architecture for Learning How to Make Contextualized Decisions" (to appear in Proc. of International Workshop MCMP 05), may 2005.
• 3: Harry Chen , Tim Finin , Anupam Joshi, An ontology for context-aware pervasive computing environments, The Knowledge Engineering Review, v.18 n.3, p.197-207, September 2003 [doi>DOI:10.1017/S0269888904000025]
• 4: Coutaz J., Ray G. -"Foundations for a theory of contextors", In Proc. CADUI02, ACM Publication, pp. 283--302, 2002.
• 5: Joëlle Coutaz , James L. Crowley , Simon Dobson , David Garlan, Context is key, Communications of the ACM, v.48 n.3, March 2005 [doi>10.1145/1047671.1047703]
• 6: Dey, A., Abowd, G.- "Towards a better understanding of Context and Context-Awareness", GVU Technical Report GIT-GVU-00-18, 1999.
• 7: Avelino J. Gonzalez , Robert Ahlers, Context-based representation of intelligent behavior in training simulations, Transactions of the Society for Computer Simulation International, v.15 n.4, p.153-166, Dec. 1998
• 8: Karen Henricksen , Jadwiga Indulska , Andry Rakotonirainy, Modeling Context Information in Pervasive Computing Systems, Proceedings of the First International Conference on Pervasive Computing, p.167-180, August 26-28, 2002
• 9: Lashkari Y. et al - "Collaborative Interface Agents", Proc. of the Third International Conference on Information and Knowledge Management CIKM'94, ACM Press, 1994
• 10: Lin S., J. Y. Hsu - "Learning User's Scheduling Criteria in a Personal Calendar Agent", Proc. of TAAI2000, Taipei.
• 11: Tom M. Mitchell , Rich Caruana , Dayne Freitag , John McDermott , David Zabowski, Experience with a learning personal assistant, Communications of the ACM, v.37 n.7, p.80-91, July 1994 [doi>10.1145/176789.176798]
• 12: OWL - http://www.w3.org/2004/OWL/
• 13: Mr. Jason Pascoe, Adding Generic Contextual Capabilities to Wearable Computers, Proceedings of the 2nd IEEE International Symposium on Wearable Computers, p.92, October 19-20, 1998
• 14: 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.
• 15: Paul Scerri , David V. Pynadath , Milind Tambe, Why the elf acted autonomously: towards a theory of adjustable autonomy, Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2, July 15-19, 2002, Bologna, Italy [doi>10.1145/544862.544944]
• 16: Tao Gu et al - "An Ontology-based Context Model in Intelligent Environments", Proc. of Communication Networks and Distributed Systems Modeling and Simulation Conference, 2004.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representation languages

Keywords:
context ontology, definition of context