Languages for the assessment of knowledge
Journal of Mathematical Psychology
Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic similarity networks
Probabilistic similarity networks
An entropy-based learning algorithm of Bayesian conditional trees
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Experimental results on user knowledge assessment with an evidential reasoning methodology
IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
An inference technique for integrating knowledge from disparate sources
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
| Hi-index | 0.01 |
Implication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or conclusions. However a major bottleneck in developing such systems lies in the elicitation of these rules. This paper empirically examines the performance of evidential inferencing with implication networks generated using a rule induction tool called KAT. KAT utilizes an algorithm for the statistical analysis of empirical case data, and hence reduces the knowledge engineering efforts and biases in subjective implication certainty assignment. The paper describes several experiments in which real-world diagnostic problems were investigated; namely, medical diagnostics. In particular, it attempts to show that (1) with a limited number of case samples, KAT is capable of inducing implication networks useful for making evidential inferences based on partial observations, and (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events.