Making important decisions often requires treating major uncertainty, long time horizons, and complex value issues. To deal with such problems, the discipline decision of analysis and knowledge-based systems have been developed at two different angles of decision system. However decision analysis has drawbacks such that the amount of effort expended and time spent on modeling a problem is too burdensome and that the resulting model is applicable to only one specific problem. This cost, in both time and money, has been a limiting factor in the use of decision analysis. Although many researchers are interested in developing methods for providing useful knowledge-based decision support, they have paid little attention to the modeling of user-specific preferences and tradeoffs about the quantitative values obtained from users. Furthermore, purely symbolic(non-quantitative) reasoning techniques have limited utility in the solution of many decision problems without the explicit consideration of the quantitative notions of uncertainty and tradeoffs.
Therefore, the aim of this research is to synthesize the methodologies of decision analysis with knowledge-based systems to utilize their benefits. To such a purpose, this dissertation suggested two integration directions:
1. Decision analysis is performed with knowledge-based systems-the knowledge of decision analyst(s) and domain expert(s) are converted into knowledge-based systems so it is possible to analyze a decision problem without the help of decision analyst and domain experts.
2. The realm of knowledge-based systems are expanded to cover the methodologies of decision analysis-the decision model is also a knowledge representation method so the methodologies of decision analysis may be compounded with the knowledge-based systems.
To replace the role of domain experts with knowledge-base, decision class analysis is formally defined. Analyzing a class of decisions makes it possible to build and analyze a dec...