Index Catalog // ScholarsArchive@OSU

Creator:: Oregon State University. Dept. of Computer Science, Bayer-Zubek, Valentina, and Dietterich, Thomas Glen
Abstract:: This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected...
Resource Type:: Research Paper
Full Text:: Engineering and Computer Science, Dearborn Hall 102, Oregon State University, Corvallis, OR 97331-3102 USA

Creator:: Oregon State University. Dept. of Computer Science and Zubek, Valentina Bayer
Abstract:: A diagnostic policy species 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....
Resource Type:: Research Paper
Full Text:: Systematic Search Valentina Bayer�Zubek School of Electrical Engineering and Computer Science Oregon State

Creator:: Oregon State University. Dept. of Computer Science, Zubek, Valentina Bayer, and Dietterich, Thomas Glen
Abstract:: This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to formulate this as a Markov Decision Process in which the transition model is learned from the training data. Specifically we assume a set...
Resource Type:: Research Paper
Full Text:: Computer Science� Oregon State University� Corvallis� OR �� USA Abstract This paper addresses cost

Creator:: Oregon State University. Dept. of Computer Science, Zubek, Valentina Bayer, and Dietterich, Thomas Glen
Abstract:: A common heuristic for solving Partially Observable Markov Decision Problems POMDPs is to first solve the underlying Markov Decision Process MDP and then construct a POMDP policy by performing a fixed depth lookahead search in the POMDP and evaluating the leaf nodes using the MDP value function. A problem with...
Resource Type:: Research Paper
Full Text:: Department of Computer Science� Oregon State University� Corvallis� OR �� USA Abstract A common

Creator:: Oregon State University. Dept. of Computer Science, Zubek, Valentina Bayer, and Dietterich, Thomas Glen
Abstract:: This paper introduces the even-odd POMDP an approximation to POMDPs Partially Observable Markov Decision Problems in which the world is assumed to be fully observable every other time step. This approximation works well for problems with a delayed need to observe. The even-odd POMDP can be converted into an equivalent...
Resource Type:: Research Paper
Full Text:: Anticipates the Need to Observe Technical Report �� Oregon State University� Dept of Computer Science

Creator:: Oregon State University. Dept. of Computer Science and Bayer, Valentina
Abstract:: "The specifi c problem addressed in this proposal is the development of good approximation algorithms for solving problems that have partial observability. The model we propose associates costs with obtaining information about the current state. We want to predict when and how much it is necessary to observe. We want...
Resource Type:: Research Paper
Full Text:: Decision Processes Ph.D. Proposal Department of Computer Science Oregon State University Valentina

Creator:: Oregon State University. Dept. of Computer Science, Bayer, Valentina, and Dietterich, Thomas Glen
Abstract:: This paper introduces the even-odd POMDP, an approximation to POMDPs in which the world is assumed to be fully observable every other time step. The even-odd POMDP can be converted into an equivalent MDP, the 2MDP, whose value function, V*[subscript 2MDP], can be combined online with a 2-step lookahead search...
Resource Type:: Research Paper
Full Text:: Science, Oregon State University, Corvallis, OR 97331 USA Abstract This paper introduces the even-odd

ScholarsArchive@OSU