Stochastic System Monitoring and Control

Gregory M. Provan
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:243-250, 2001.

Abstract

In this article we propose a new technique for efficiently solving a specialized instance of a finite state sequential decision process. This specialized task requires keeping a system within a set of nominal states, introducing control actions only when forbidden states are entered. Instead of assuming that the process evolves only due to control actions, we assume that system evolution occurs due to both internal system dynamics and control actions, referred to as endogenous and exogenous evolution respectively. Since controls are needed only for exogenous evolution, we separate inference for the case of endogenous and exogenous evolution, obtaining an inference method that is computationally simpler than using a standard POMDP framework for solving this task. We summarize the problem framework and the algorithm for performing sequential decision-making.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR3-provan01a, title = {Stochastic System Monitoring and Control}, author = {Provan, Gregory M.}, booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics}, pages = {243--250}, year = {2001}, editor = {Richardson, Thomas S. and Jaakkola, Tommi S.}, volume = {R3}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r3/provan01a/provan01a.pdf}, url = {https://proceedings.mlr.press/r3/provan01a.html}, abstract = {In this article we propose a new technique for efficiently solving a specialized instance of a finite state sequential decision process. This specialized task requires keeping a system within a set of nominal states, introducing control actions only when forbidden states are entered. Instead of assuming that the process evolves only due to control actions, we assume that system evolution occurs due to both internal system dynamics and control actions, referred to as endogenous and exogenous evolution respectively. Since controls are needed only for exogenous evolution, we separate inference for the case of endogenous and exogenous evolution, obtaining an inference method that is computationally simpler than using a standard POMDP framework for solving this task. We summarize the problem framework and the algorithm for performing sequential decision-making.}, note = {Reissued by PMLR on 31 March 2021.} }
Endnote
%0 Conference Paper %T Stochastic System Monitoring and Control %A Gregory M. Provan %B Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2001 %E Thomas S. Richardson %E Tommi S. Jaakkola %F pmlr-vR3-provan01a %I PMLR %P 243--250 %U https://proceedings.mlr.press/r3/provan01a.html %V R3 %X In this article we propose a new technique for efficiently solving a specialized instance of a finite state sequential decision process. This specialized task requires keeping a system within a set of nominal states, introducing control actions only when forbidden states are entered. Instead of assuming that the process evolves only due to control actions, we assume that system evolution occurs due to both internal system dynamics and control actions, referred to as endogenous and exogenous evolution respectively. Since controls are needed only for exogenous evolution, we separate inference for the case of endogenous and exogenous evolution, obtaining an inference method that is computationally simpler than using a standard POMDP framework for solving this task. We summarize the problem framework and the algorithm for performing sequential decision-making. %Z Reissued by PMLR on 31 March 2021.
APA
Provan, G.M.. (2001). Stochastic System Monitoring and Control. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R3:243-250 Available from https://proceedings.mlr.press/r3/provan01a.html. Reissued by PMLR on 31 March 2021.

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