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Preference-Based Fault Estimation in Autonomous Robots : Incom-pleteness and Meta-Diagnosis

Abstract : In autonomous systems, planning and decision making rely on the estimation of the system state across time. In this work, we use a preference model to implement a fault management strategy that selects a unique estimated state at each time point. If this strategy is not carefully designed, it can lead to incomplete estimators that meet a dead-end in some scenarios. Our goal is to detect such scenarios at design time and to be able to blame a subset of preferences causing them; those can be proposed to the designer for revision. To do so, we propose a method for detecting dead-end scenarios, introduce preference relaxation, and apply a consistency-based meta-diagnosis approach for identifying the sets of "faulty" preferences for a given dead-end scenario. We build upon SAT solvers for checking estimator incompleteness, and for consistency checking during meta-diagnosis.
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Contributor : Louise Travé-Massuyès <>
Submitted on : Wednesday, November 27, 2019 - 5:56:12 PM
Last modification on : Thursday, June 10, 2021 - 3:05:48 AM


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  • HAL Id : hal-02383526, version 1


Valentin Bouziat, Xavier Pucel, Stéphanie Roussel, Louise Travé-Massuyès. Preference-Based Fault Estimation in Autonomous Robots : Incom-pleteness and Meta-Diagnosis. 18th International Conference on Autonomous Agents and Multi-Agent Systems, May 2019, Montreal, Canada. ⟨hal-02383526⟩



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