Geometric modeling of a nuclear environment

Jan De Geeter, Marc Decréton, Joris De Schutter, Herman Bruyninckx, Hendrik Van Brussel
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2, 1999.

Abstract

This paper is about the task-directed updating of an incomplete and inaccurate geometric model of a nuclear environment, using only robust radiation-resistant sensors installed on a robot that is remotely controlled by a human operator. In this problem, there are many sources of uncertainty and ambiguity. This paper proposes a probabilistic solution under Gaussian assumptions. Uncertainty is reduced with an estimator based on a Kalman filter. Ambiguity on the measurement-feature association is resolved by running a bank of those estimators in parallel, one for each plausible association. The residual errors of these estimators are used for hypothesis testing and for the calculation of a probability distribution over the remaining hypotheses. The best next sensing action is calculated as a Bayes decision with respect to a loss function that takes into account both the uncertainty on the current estimate, and the variance/precision required by the task.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR2-geeter99a, title = {Geometric modeling of a nuclear environment}, author = {Geeter, Jan De and Decr{\'{e}}ton, Marc and Schutter, Joris De and Bruyninckx, Herman and Brussel, Hendrik Van}, booktitle = {Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics}, year = {1999}, editor = {Heckerman, David and Whittaker, Joe}, volume = {R2}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r2/geeter99a/geeter99a.pdf}, url = {https://proceedings.mlr.press/r2/geeter99a.html}, abstract = {This paper is about the task-directed updating of an incomplete and inaccurate geometric model of a nuclear environment, using only robust radiation-resistant sensors installed on a robot that is remotely controlled by a human operator. In this problem, there are many sources of uncertainty and ambiguity. This paper proposes a probabilistic solution under Gaussian assumptions. Uncertainty is reduced with an estimator based on a Kalman filter. Ambiguity on the measurement-feature association is resolved by running a bank of those estimators in parallel, one for each plausible association. The residual errors of these estimators are used for hypothesis testing and for the calculation of a probability distribution over the remaining hypotheses. The best next sensing action is calculated as a Bayes decision with respect to a loss function that takes into account both the uncertainty on the current estimate, and the variance/precision required by the task.}, note = {Reissued by PMLR on 20 August 2020.} }
Endnote
%0 Conference Paper %T Geometric modeling of a nuclear environment %A Jan De Geeter %A Marc Decréton %A Joris De Schutter %A Herman Bruyninckx %A Hendrik Van Brussel %B Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1999 %E David Heckerman %E Joe Whittaker %F pmlr-vR2-geeter99a %I PMLR %U https://proceedings.mlr.press/r2/geeter99a.html %V R2 %X This paper is about the task-directed updating of an incomplete and inaccurate geometric model of a nuclear environment, using only robust radiation-resistant sensors installed on a robot that is remotely controlled by a human operator. In this problem, there are many sources of uncertainty and ambiguity. This paper proposes a probabilistic solution under Gaussian assumptions. Uncertainty is reduced with an estimator based on a Kalman filter. Ambiguity on the measurement-feature association is resolved by running a bank of those estimators in parallel, one for each plausible association. The residual errors of these estimators are used for hypothesis testing and for the calculation of a probability distribution over the remaining hypotheses. The best next sensing action is calculated as a Bayes decision with respect to a loss function that takes into account both the uncertainty on the current estimate, and the variance/precision required by the task. %Z Reissued by PMLR on 20 August 2020.
APA
Geeter, J.D., Decréton, M., Schutter, J.D., Bruyninckx, H. & Brussel, H.V.. (1999). Geometric modeling of a nuclear environment. Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R2 Available from https://proceedings.mlr.press/r2/geeter99a.html. Reissued by PMLR on 20 August 2020.

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