Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2005
Abstract
It is a frequently encountered problem that new knowledge arrived when making decisions in a dynamic world. Usually, domain experts cannot afford enough time and knowledge to effectively assess and combine both qualitative and quantitative information in these models. Existing approaches can solve only one of two tasks instead of both. We propose a four-step algorithm to integrate multiple probabilistic graphic models, which can effectively update existing models with newly acquired models. In this algorithm, the qualitative part of model integration is performed first, followed by the quantitative combination. We illustrate our method with an example of combining three models. We also identify the factors that may influence the complexity of the integrated model. Accordingly, we identify three factors that may influence the complexity of the integrated model. Accordingly, we present three heuristic methods of target variable ordering generation. Such methods show their feasibility through our experiments and are good in different situations. Finally, we provide some comments based on our experiments results.
Discipline
Artificial Intelligence and Robotics | Computer Sciences
Publication
Metacognition in computation: Papers from the 2005 AAAI Symposium
First Page
40
Last Page
47
ISBN
9781577352303
Publisher
AAAI Press
City or Country
Palo Alto, CA, USA
Citation
Jiang C., Poh K., and Tze-Yun LEONG.
Integration of Probabilistic Graphic Models for Decision Support. (2005). Metacognition in computation: Papers from the 2005 AAAI Symposium. 40-47.
Available at: https://ink.library.smu.edu.sg/sis_research/3022
Creative Commons License
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