Advertisement

Learning Classifier System for Generating Various Types of Dialogues in Conversational Agent

  • Eun-Kyung Yun
  • Sung-Bae Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)

Abstract

Most of the conversational agents respond to the users in an unsatisfactory way because of using the simple sequential pattern matching. In this paper, we propose a conversational agent that can respond with various sentences for improving the user’s familiarity. The agent identifies the user’s intention using DA (Dialogue Acts) and increases the intelligence and the variety of the conversation using LCS (Learning Classifier System). We apply this agent to the introduction of a web site. The results show that the conversational agent has the ability to present more adequate and friendly response to user’s query.

Keywords

Learn Classifier System Conversational Agent Classifier List Matching Classifier Answer Sentence 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Weizenbaun, J.: ELIZA - A Computer Program for the Study of Natural Language Communication between Man and Machine. Communications of the ACM 9(1), 36–45 (1965)CrossRefGoogle Scholar
  2. 2.
    Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier Systems and Genetic Algorithms. Artificial Intelligence 40, 235–282 (1989)CrossRefGoogle Scholar
  3. 3.
    Katagami, D., Yamada, S.: Interactive Classifier System for Real Robot Learning. In: Proceedings of the 2000 IEEE International Workshop on Robot and Human Interactive Communication, pp. 258–263 (2000)Google Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  5. 5.
    McAulay, A.D., Oh, J.C.: Improving Learning of Genetic Rule-Based Classifier Systems. IEEE Transactions on Systems, Man, and Cybernetics 24(1), 152–159 (1994)CrossRefGoogle Scholar
  6. 6.
    Haung, D.: A Framework for the Credit-Apportionment Process in Rule-Based Systems. IEEE Transactions on Systems, Man, and Cybernetics 19(3), 489–498 (1989)CrossRefGoogle Scholar
  7. 7.
    Core, M.G., Allen, J.F.: Coding Dialogs with the DAMSL Annotation Scheme. In: Working Notes of the AAAI Fall Symposium on Communicative Action in Humans and Machines, pp. 28–35 (1997)Google Scholar
  8. 8.
    Lee, S.-I., Cho, S.-B.: An Intelligent agent with structured pattern matching for a virtual representative. In: Intelligent Agent Technology, Maebashi, Japan, October 2001, pp. 305–309 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Eun-Kyung Yun
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

Personalised recommendations