Using model uncertainty for robust optimization in approximate inference control

Using model uncertainty for robust optimization in approximate inference control Recently, the optimization-by-inference approach has been proposed as a new means for solving high-dimensional optimization problems quickly. Approximate Inference COntrol (AICO) is one of the most successful and promising methods that implement the optimization-by-inference approach. AICO is able to solve stochastic optimal control problems and has already been successfully used in many applications. However, it is known that the iterative inference of AICO sometimes fails to converge to the optimal solution. To make the optimization more robust, in this paper, we propose to take model uncertainty into account. In AICO, the cost function to be minimized is accurate around a particular state of a given stochastic system, but the accuracy is uncertain in regions far from that state. Because using such an uncertain function is harmful to the convergence, we modify AICO, so that it does not use the function in uncertain regions. Our method is easy to implement and does not add much computational time to the original AICO. Experiments using two different scenarios show that our method substantially improves AICO in terms of the rate at which the algorithm produces convergent results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Life and Robotics Springer Journals

Using model uncertainty for robust optimization in approximate inference control

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Publisher
Springer Japan
Copyright
Copyright © 2017 by ISAROB
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Computation by Abstract Devices; Control, Robotics, Mechatronics
ISSN
1433-5298
eISSN
1614-7456
D.O.I.
10.1007/s10015-017-0361-6
Publisher site
See Article on Publisher Site

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