Principles of artificial intelligence
Principles of artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision-theoretic troubleshooting
Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning cost-sensitive active classifiers
Artificial Intelligence
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Journal of Artificial Intelligence Research
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Myopic value of information in influence diagrams
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning policies for sequential time and cost sensitive classification
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
VOILA: efficient feature-value acquisition for classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Integrating learning from examples into the search for diagnostic policies
Journal of Artificial Intelligence Research
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Approximate nonmyopic sensor selection via submodularity and partitioning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Efficient sensor selection for active information fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Journal of Artificial Intelligence Research
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A diagnostic policy specifies what test to perform next, based on the results of previous tests, and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the costs of tests and (b) the costs of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process (MDP). We investigate two types of algorithms for solving this MDP: systematic search based on the AO* algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples, but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed that control overfitting and speed up the search. This research is the first that integrates overfitting prevention into systematic search. The paper has two contributions: it discusses the factors that make systematic search feasible for diagnosis, and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods.