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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>David Heckerman</author_name><author_url>https://www.microsoft.com/en-us/research/people/heckerma/</author_url><title>Troubleshooting Under Uncertainty - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="lA8odzysGM"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/troubleshooting-under-uncertainty/"&gt;Troubleshooting Under Uncertainty&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/troubleshooting-under-uncertainty/embed/#?secret=lA8odzysGM" width="600" height="338" title="&#x201C;Troubleshooting Under Uncertainty&#x201D; &#x2014; Microsoft Research" data-secret="lA8odzysGM" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>We develop a series of approximations for decision-theoretic troubleshooting under uncertainty. Our approach generates troubleshooting plans in the face of uncertainty in the relationships among components and device status, observations, as well as the affect of actions on device status. Included in our approach is a Bayesian-network representation of these relationships. We have applied our [&hellip;]</description></oembed>
