A practical guide to designing expert systems
A practical guide to designing expert systems
Software engineering: reliability, development, and management.
Software engineering: reliability, development, and management.
A guide to expert systems
Building expert systems
Evaluation of competing software reliability predictions
IEEE Transactions on Software Engineering - Special issue on reliability and safety in real-time process control
Conflicting information integration for decision support
Decision Support Systems
Algorithms for clustering data
Algorithms for clustering data
Systems analysis techniques for the implementation of expert systems
Information and Software Technology
Practical engineering of knowledge-based systems
Information and Software Technology
Testing in the program development cycle
Software Engineering Journal
Clustering of homogeneous subsets
Pattern Recognition Letters
Knowledge-based system design enhancement through reliability measurement
Knowledge-based system design enhancement through reliability measurement
Computer science as empirical inquiry: symbols and search
Communications of the ACM
Clustering Algorithms
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Many knowledge based systems are designed and built with little attention paid to the reliability of the output. In this paper, we present an approach, using partitioning of both the knowledge base and the input space, that allows for the measurement of the reliability during any program increment in a rapid prototyping development cycle. Before presenting the approach, we formalize the problem using concepts from general systems theory and then describe our three objectives: 1) measurement of the reliability of the knowledge-based system at the current program increment, 2) prediction of the reliability of the future system, and 3) support for design decisions.Finally, we apply our approach to a design-aiding knowledge-based system for the selection of materials under various climatic conditions. The design-aiding knowledge-based system is used by U.S. Army personnel in the development of equipment to be used by the U.S. Army in various regions of the world. We find that the current system, containing 40 rules, has a reliability of approximately 0.85. However, more importantly, we have discovered the rules that led to many of the failures.