Automated combination of probabilistic graphic models from multiple knowledge sources

Abstract

It is a frequently encountered problem that new knowledge arrives when making decisions in a dynamic world. Bayesian networks and influence diagrams, two major probabilistic graph models, are powerful representation and reasoning tools for complex decision problems. 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 the two tasks instead of both. Based on an extensive literature survey, 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 quantitative combination. We illustrate our method with a comprehensive example in a real domain. We also identify some 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. Furthermore, we discuss influence diagram combination and present a utility-based method to combine probability distributions. Finally, we provide some comments based on our experiments results.