A conceptual model for inexact reasoning in rule-based systems
International Journal of Approximate Reasoning
Journal of Logic Programming
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Logical Foundations for Rule-Based Systems (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Discovering Medical Knowledge from Data in Patients' Files
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
A rule-based implementation of fuzzy tableau reasoning
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
Using semantic data integration to create reliable rule-based systems with uncertainty
Engineering Applications of Artificial Intelligence
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There are many places (e.g. hospital emergency rooms) where reliable diagnostic systems might support people in their work. The paper discusses the problem of designing diagnostic rule-based systems with uncertainty. Most such systems use the technique of forward chaining in their reasonings. The number and the contents of the hypotheses depend then on both the form of system's knowledge base and the details of the inference engine performance. In particular, the hypotheses can be influenced by the rules' priorities. In the paper we propose a method for determining priorities for the rules designed from true evidence base which contains aggregate data of an attributive representation.