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The senior companion multiagent dialogue system

Published: 12 May 2008 Publication History

Abstract

This article presents a multi-agent dialogue system. We show how a collection of relatively simple agents is able to treat complex dialogue phenomena and deal successfully with different deployment configurations. We analyze our system regarding robustness and scalability. We show that it degrades gracefully under different failures and that the architecture allows one to easily add new modalities was well as porting the system to different applications and platforms.

References

[1]
Cunningham, H., D. Maynard, et al. Framework and Graphical Development Environment for Robust NLP. Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL'02). Philadelphia. (2002).
[2]
Wahlster, W. (Ed). SmartKom: Foundations of Multimodal Dialogue Systems. (2006). Springer-Verlag.
[3]
Turunen, M et al. An Architecture and applications for Speech-based accessibility systems. IBM Systems Journal Vol 44, N 3, (2005).
[4]
Houlette, R. and Fu, D. "The Ultimate Guide to FSMs in Games". AI Game Programming Wisdom 2, Steve Rabin (Ed). 2003.
[5]
Catizone, R., Setzer, A., and Wilks, Y. "Multimodal Dialogue Management in the COMIC Project", Workshop on Dialogue Systems: interaction, adaptation and styles of management. (EACL)Budapest, Hungary. (2003)
[6]
Guessoum, Z., Briot, J.-P. From Active Objects to Autonomous Agents. IEEE Concurrency 7, 3 (1999) 68--76
[7]
William A. Woods: Transition network grammars for natural language analysis. Commun.ACM 13(10): 591--606 (1970)

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Published In

cover image ACM Conferences
AAMAS '08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
May 2008
503 pages
ISBN:9780981738123

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 12 May 2008

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Author Tags

  1. agent-oriented software engineering
  2. case studies
  3. dialogue
  4. emergent behavior
  5. natural language processing
  6. performance
  7. robustness
  8. scalability

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  • Research-article

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AAMAS08
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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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