Conference Proceeding Article
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.
Artificial Intelligence and Robotics | Computer Sciences
Intelligent Systems and Decision Analytics
Metacognition in computation: Papers from the 2005 AAAI Symposium
City or Country
Palo Alto, CA, USA
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3022