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Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies
As humans come to rely on autonomous systems more, ensuring the transparency of such systems is important to their continued adoption.
Recursively-Constrained Partially Observable Markov Decision Processes
Evaluations on a set of benchmark problems demonstrate the efficacy of our algorithm and show that policies for RC-POMDPs produce more desirable behaviors than policies for C-POMDPs.
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