Parameter learning in Bayesian network using semantic constraints of conversational feedback

Seung Hyun Lee, Sungsoo Lim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Many learning techniques of Bayesian network have been developed for adaptation to user or environment. However, it seems several drawbacks still exists in conventional learning approach; the hardness of collecting log data, the inherent ambiguity in recognizing and reflecting implicit user' s intention, and difficulties in extracting relations between data or definite rules. In this paper, we propose a method for parameter learning in Bayesian network using semantic constraints of conversational feedback to overcome these limitations. Production rules extracted from users' conversational feedback are used in parameter learning of Bayesian network. A comparison test with conventional approaches in conducted to verify the usefulness of the proposed method.

Original languageEnglish
Title of host publicationPRICAI 2010
Subtitle of host publicationTrends in Artificial Intelligence - 11th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Pages467-476
Number of pages10
DOIs
Publication statusPublished - 2010 Nov 3
Event11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010 - Daegu, Korea, Republic of
Duration: 2010 Aug 302010 Sep 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6230 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010
CountryKorea, Republic of
CityDaegu
Period10/8/3010/9/2

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, S. H., Lim, S., & Cho, S. B. (2010). Parameter learning in Bayesian network using semantic constraints of conversational feedback. In PRICAI 2010: Trends in Artificial Intelligence - 11th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 467-476). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6230 LNAI). https://doi.org/10.1007/978-3-642-15246-7_43