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Online Inference for Adaptive Diagnosis via Arithmetic Circuit Compilation of Bayesian Networks

Sara Zermani 1 Catherine Dezan 2 Reinhardt Euler 1 Jean-Philippe Diguet 3
1 Lab-STICC_UBO_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance, UBO - Université de Brest
3 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Considering technology and complexity evolution the design of fully reliable embedded systems will be prohibitively complex and costly. Onboard diagnosis is a first solution that can be achieved by means of Bayesian networks. An efficient compilation of Bayesian inference is proposed using Arithmetic Circuits (AC). ACs can be efficiently implemented in hardware to get very fast response time. This approach has been recently experimented in Software Health Management of aircrafts or UAVs. However, there are two kinds of obstacles that must be addressed. First, the tree complexity can lead to intractable solutions and second, an offline static analysis cannot capture the dynamic behaviour of a system that can have multiple configurations and applications. In this paper, we present our direction to solve these issues. Our approach relies on an adaptive version of the diagnosis computation for different kinds of applications/missions of UAVs. In particular, we consider an incremental generation of the AC structure. This adaptive diagnosis can be implemented using dynamic reconfiguration of FPGA circuits.
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https://hal.univ-brest.fr/hal-00965533
Contributor : Catherine Dezan <>
Submitted on : Tuesday, March 25, 2014 - 12:48:42 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:23 PM
Long-term archiving on: : Wednesday, June 25, 2014 - 11:40:54 AM

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  • HAL Id : hal-00965533, version 1

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Sara Zermani, Catherine Dezan, Reinhardt Euler, Jean-Philippe Diguet. Online Inference for Adaptive Diagnosis via Arithmetic Circuit Compilation of Bayesian Networks. Designing with Uncertainty: Opportunities & Challenges workshop, Mar 2014, York, United Kingdom. ⟨hal-00965533⟩

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