Diagnostic reasoning based on structure and behavior
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
The use of design descriptions in automated diagnosis
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
An artificial intelligence approach to test generation
An artificial intelligence approach to test generation
A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Inductive learning in probabilistic domain
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Multiple dimensions of generalization in model-based troubleshooting
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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This paper describes a model-based diagnostic system architecture which has test generation capability and which learns from experience. It acquires experiential knowledge from single experiences in the form of compact symptom-failure association rules and component failure records. With this capability, when it has had a similar experience in the past, it can diagnose the failure faster and more efficiently by suggesting better tests. Even if it has not had any similar experience in the past, it can diagnose the failure using its knowledge about the structure and behavior of the target system. Therefore, this architecture gives a solution to both the knowledge acquisition bottleneck problem of heuristic rule based systems and the efficiency problem of model-based systems. Experimental results show this technique to be practical and promising. It has been implemented in PROLOG in a concise form.