The Role of Tuning Uncertain Inference Systems
This work addresses the problem of parameter tuning in uncertain inference systems for AI researchers, but it is incremental as it builds on existing models without introducing new methods.
The study compared the performance of four uncertain inference systems (MYCIN, PROSPECTOR, an independence model, and a linear equation) by tuning their parameters to approximate a full probability model on simple networks, finding that the independence model was more accurate while the others performed equivalently.
This study examined the effects of "tuning" the parameters of the incremental function of MYCIN, the independent function of PROSPECTOR, a probability model that assumes independence, and a simple additive linear equation. me parameters of each of these models were optimized to provide solutions which most nearly approximated those from a full probability model for a large set of simple networks. Surprisingly, MYCIN, PROSPECTOR, and the linear equation performed equivalently; the independence model was clearly more accurate on the networks studied.