Improving Probability Estimation Through Active Probabilistic Model Learning

Jingyi Wang, Xiaohong Chen, Jun Sun, Shengchao Qin

Research output: Contribution to journalArticle

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Abstract

It is often necessary to estimate the probability of certain events occurring in a system. For instance, knowing the probability of events triggering a shutdown sequence allows us to estimate the availability of the system. One approach is to run the system multiple times and then construct a probabilistic model to estimate the probability. When the probability of the event to be estimated is low, many system runs are necessary in order to generate an accurate estimation. For complex cyber-physical systems, each system run is costly and time-consuming, and thus it is important to reduce the number of system runs while providing accurate estimation. In this work, we assume that the user can actively tune the initial configuration of the system before the system runs and answer the following research question: how should the user set the initial configuration so that a better estimation can be learned with fewer system runs. The proposed approach has been implemented and evaluated with a set of benchmark models, random generated models, and a real-world water treatment system.
Original languageEnglish
Pages (from-to)-
JournalLecture Notes in Computer Science
Publication statusPublished - 11 Oct 2017

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