Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Case-based reasoning
A probabilistic framework for memory-based reasoning
Artificial Intelligence
Applying Case-Based Reasoning to Autoclave Loading
IEEE Expert: Intelligent Systems and Their Applications
Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks
Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks
Intelligent decision support for protein crystal growth
IBM Systems Journal - Deep computing for the life sciences
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Prototype-based classification
Applied Intelligence
A comparative study of catalogue-based classification
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Optimising retrieval phase in CBR through Pearl and JLO algorithms for medical diagnosis
International Journal of Advanced Intelligence Paradigms
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Bayesian Case Reconstruction (BCR) is a case-based technique that broadens the coverage of a case library by sampling and recombining pieces of existing cases to construct a large set of "plausible" cases. It employs a Bayesian Belief Network to evaluate whether implicit dependencies within the original cases have been maintained. The belief network is constructed from the expert's limited understanding of the domain theory combined with the data available in the case library. The cases are the primary reasoning vehicle. The belief network leverages the available domain model to help evaluate whether the "plausible" cases have maintained the necessary internal context. BCR is applied to the design of screening experiments for Macromolecular Crystallization in the Probabilistic Screen Design program. We describe BCR and provide an empirical comparison of the Probabilistic Screen Design program against the current practice in Macromolecular Crystallization.