Multi-layer bayesian network for variable-bound inference

Shizhuo Zhu, Po Chun Chen, John Yen

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

2 Scopus citations

Abstract

Agent decision-making is an information-intensive activity. Its performance is affected by the availability of relevant information. Bayesian networks have provided a probabilistic estimate for uncertain information. However, for those decision problems where information is represented in predicates, Bayesian inferences are required to process the variable-bound relations across predicates. Multi-Layer Bayesian Network (MLBN) is an extension of the classical model of Bayesian networks with multiple layers of conditional probability tables, each corresponding to one specific variable binding. The MLBN has been implemented based on an agent architecture. Experiments have shown its capability of improving performance in an experience-based decision-making framework.

Original languageEnglish (US)
Title of host publicationProceedings - 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2008
Pages553-559
Number of pages7
DOIs
Publication statusPublished - Dec 1 2008
Event2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2008 - Sydney, NSW, Australia
Duration: Dec 9 2008Dec 12 2008

Publication series

NameProceedings - 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2008

Other

Other2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2008
CountryAustralia
CitySydney, NSW
Period12/9/0812/12/08

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software

Cite this

Zhu, S., Chen, P. C., & Yen, J. (2008). Multi-layer bayesian network for variable-bound inference. In Proceedings - 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2008 (pp. 553-559). [4740683] (Proceedings - 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2008). https://doi.org/10.1109/WIIAT.2008.306