Automatic knowledge base refinement for classification systems
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The engineering of knowledge-based systems: theory and practice
The engineering of knowledge-based systems: theory and practice
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Refinement complements verification and validation
International Journal of Human-Computer Studies - Special issue: verification and validation
On Learning Read-k-Satisfy-j DNF
SIAM Journal on Computing
The complexity of theory revision
Artificial Intelligence
Representing problem-solving for knowledge refinement
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Verification and validation of Bayesian knowledge-bases
Data & Knowledge Engineering
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Verification and Validation of Rule-Based Expert Systems
Verification and Validation of Rule-Based Expert Systems
Knowledge Validation: Principles and Practice
IEEE Expert: Intelligent Systems and Their Applications
KJ3: a tool assisting formal validation of knowledge-based systems
International Journal of Human-Computer Studies
Identifying and Handling Structural Incompleteness for Validation of Probabilistic Knowledge-Bases
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
A Quagmire of Terminology: Verification and Validation, Testing, and Evaluation
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Higher Order Refinement Heuristics for Rule Validation
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
System Refinement in Practice - Using a Formal Method to Modify Real-Life Knowledge
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
FlexiMine – A Flexible Platform for KDD Research and Application Development
Annals of Mathematics and Artificial Intelligence
GESIA: Uncertainty-Based Reasoning for a Generic Expert System Intelligent User Interface
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Reasoning with BKBs – Algorithms and Complexity
Annals of Mathematics and Artificial Intelligence
Consistency of Test Cases in Validation of Bayesian Knowledge-Bases
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Bias-driven revision of logical domain theories
Journal of Artificial Intelligence Research
Refinement of uncertain rule bases via reduction
International Journal of Approximate Reasoning
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A framework for validation of rule-based systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Deterministic approximation of marginal probabilities in Bayes nets
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On a framework for the prediction and explanation of changing opinions
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Knowledge-Based Systems
Fusing multiple Bayesian knowledge sources
International Journal of Approximate Reasoning
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Knowledge validation, as part of knowledge base verification and validation, is a critical process in knowledge engineering. The ultimate goal of this process is to make the knowledge base satisfy all test cases given by human experts. This is further complicated by factors such as uncertainty and incompleteness. Our paper covers theoretical results in knowledge validation for Bayesian Knowledge Bases (BKBs), a probabilistic model extended from Bayesian Networks for representing knowledge in uncertain domains. First, we study the consistency of test case sets by identifying the necessary and sufficient conditions for a test case set such that there exists a knowledge base satisfying all of its test cases. Second, we analyze the thrashing problem which is the interminable oscillation of the knowledge base's state when validating by parameter refinement. We propose an approach to validating BKBs that effectively eliminates thrashing under certain conditions of the original knowledge base and the test case set.