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Filtering by: Creator Bayer, Valentina Subject Markov processes

Approximation algorithms for solving cost observable Markov decision processes

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
A POMDP approximation algorithm that anticipates the need to observe

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