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Using Surprise Index for Competency Assessment in Autonomous Decision-Making
This paper considers the problem of evaluating an autonomous system's competency in performing a task, particularly when working in dynamic and uncertain environments.
Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation
We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.
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