Porfírio Filipe, Luís Morgado, Nuno Mamede



This paper describes the recent effort to improve our Domain Knowledge Manager (DKM) that is part of a mixed-initiative task based Spoken Dialogue System (SDS) architecture, namely to interact within an ambient intelligence scenario. Machine-learning applied to SDS dialogue management strategy design is a growing research area. Training of such strategies can be done using human users or using corpora of human computer dialogue. However, the size of the state space grows exponentially according to the state variables taken into account, making the task of learning dialogue strategies for large-scale SDS very difficult. To address that problem, we propose a divide to conquer approach, assuming that practical dialogue and domain-independent hypothesis are true. In this context, we have considered a clear separation between linguistic dependent and domain dependent knowledge, which allows reducing the complexity of SDS typical components, specially the Dialoguer Manager (DM). Our contribution enables domain portability issues, proposing an adaptive DKM to simplify DM’s clarification dialogs. DKM learns, through trial-and-error, from the interaction with DM suggesting a set of best task-device pairs to accomplish a request and watching the user’s confirmation. This adaptive DKM has been tested in our domain simulator.


  1. Allen, J., Byron, D., Dzikovska, M., Ferguson, G., Galescu, L., Stent, A., 2000. An Architecture for a generic dialogue shell. Natural Language Engineering, 6(3-4):213-228.
  2. Daille, B., Gaussier, E., Lange, J., 1994. Towards Automatic Extraction of Monolingual and Bilingual Terminology. In 15th International Conference on Computational Linguistics. Kyoto, Japan.
  3. Denecke, M., 2002. Rapid Prototyping for Spoken Dialog Systems. In COLING'02, 19th International Conference on Computational Linguistics. Taipei, Taiwan.
  4. Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J. and Burgelman, J-C., 2001. Scenarios for Ambient Intelligence in 2010. IST Advisory Group Report. IPTSSeville (Institute for Prospective Technological Studies).
  5. Dzikovska, M., Allen, J., Swift, M., 2003. Integrating linguistic and domain knowledge for spoken dialog systems in multiple domains. In IJCAI'03, 18th International Joint Conference on Artificial Intelligence. Acapulco, Mexico.
  6. Fellbaum, C. (editor), 1998. WordNet: An Electronic Lexical Database. MIT Press.
  7. Feng, J., Bangalore, S., Rahim, M., 2003. Webtalk: Mining Websites for Automatically Building Dialog Systems. In IEEE ASRU, Automatic Speech Recognition and Understanding Workshop. United States, Virgin Islands.
  8. Fensel, D., Benjamins, V., Motta, E., Wielinga, B., 1999. UPML: A Framework for Knowledge System Reuse. In 16th International Joint Conference on Artificial Intelligence. Stockholm, Sweden.
  9. Filipe, P., Mamede, N., 2006a. A Framework to Integrate Ubiquitous Knowledge Modeling. In 5th International Conference on Language Resources and Evaluation. Genoa, Italy.
  10. Filipe, P., Mamede, N., 2006b. Hybrid Knowledge Modeling for Ambient Intelligence. In 9th Workshop User Interfaces for All (ERCIM-UI4ALL) Special Theme: "Universal Access in Ambient Intelligence Environments". Königswinter, Germany. Springer Verlag.
  11. Filipe, P., Mamede, N., 2006c. Ubiquitous Knowledge Modeling for Dialogue Systems. In ICEIS 2006, 8th International Conference on Enterprise Information Systems. Paphos, Cyprus.
  12. Flycht-Eriksson, A., Jönsson, A., 2000. Dialogue and Domain Knowledge Management in Dialogue Systems. In 1st SIGdial Workshop on Discourse and Dialogue, Hong Kong.
  13. Gruber, T., 1992. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. In International Workshop on Formal Ontology. Padova, Italy.
  14. Henderson, J., Lemon, O., Georgila, K., 2005. Hybrid Reinforcement/Supervised Learning for Dialogue Policies from Communicator Data. In IJCAI'05, 19th International Joint Conference on Artificial Intelligence Workshop on KRPDS. Edinburgh, Scotland.
  15. McTear, M., 2004. Spoken Dialogue Technology: Towards the Conversational User Interface. Springer Verlag.
  16. Morgado, L., Gaspar, G., 2004. Focusing Reasoning Through Emotional Mechanisms. In ECAI'04, 16th European Conference on Artificial Intelligence. IOS Press. Valencia, Spain.
  17. Neto, J., Mamede, N., Cassaca, R. and Oliveira, L., 2003. The Development of a Multi-purpose Spoken Dialogue System. In Eurospeech 3003, 8th European Conference on Speech Communication and Technology, Geneva, Switzerland.
  18. Schatzmann, J., Weilhammer, K., Stuttle, M., Young, S., 2006. A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies. The Knowledge Engineering Review, 21(2):97 126.
  19. Staddon, J., 2001. Adaptive dynamics: the theoretical analysis of behavior. MIT Press.
  20. Sutton, R., 1988. Learning to Predict by the Method of Temporal Differences. Machine Learning, 3(1):9-44.
  21. Sutton, R., Barto, A., 1998. Reinforcement Learning. MIT Press.

Paper Citation

in Harvard Style

Filipe P., Morgado L. and Mamede N. (2007). AN ADAPTIVE DOMAIN KNOWLEDGE MANAGER FOR DIALOGUE SYSTEMS . In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 5: ICEIS, ISBN 978-972-8865-92-4, pages 45-52. DOI: 10.5220/0002384200450052

in Bibtex Style

author={Porfírio Filipe and Luís Morgado and Nuno Mamede},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 5: ICEIS,},

in EndNote Style

JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 5: ICEIS,
SN - 978-972-8865-92-4
AU - Filipe P.
AU - Morgado L.
AU - Mamede N.
PY - 2007
SP - 45
EP - 52
DO - 10.5220/0002384200450052