Meta-diagnosis via Preference Relaxation for State Trackability
Abstract
In autonomous systems, planning and decision making rely on the estimation of the system state across time, i.e. state tracking. In this work, a preference model is used to provide non ambiguous estimates at each time point. However, this strategy can lead to dead-ends. Our goal is to anticipate dead-ends at design time and to blame root cause preferences, so that these preferences can be revised. To do so, we present the preference-based state estimation approach and we apply a consistency-based meta-diagnosis strategy based on preference relaxation. We evaluate our approach on a robotic functional architecture benchmark.
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