POMDP policies can be checked for robustness to observation model changes by solving a bi-level optimization via root-finding with the Robust Interval Search algorithm, which runs in polynomial time for non-sticky history-independent deviations when using finite-state controllers.
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A method automatically constructs a causal model from behavior tree structure and domain knowledge to generate real-time causal counterfactual explanations for robot decisions.
Kaplan-Meier-based non-parametric estimators for ARL and ADD in quickest changepoint detection are derived with bias bounds and shown to be asymptotically unbiased for finite sequences without extrapolation.
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Robustness Analysis of POMDP Policies to Observation Perturbations
POMDP policies can be checked for robustness to observation model changes by solving a bi-level optimization via root-finding with the Robust Interval Search algorithm, which runs in polynomial time for non-sticky history-independent deviations when using finite-state controllers.
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Temporal Counterfactual Explanations of Behaviour Tree Decisions
A method automatically constructs a causal model from behavior tree structure and domain knowledge to generate real-time causal counterfactual explanations for robot decisions.
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Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis
Kaplan-Meier-based non-parametric estimators for ARL and ADD in quickest changepoint detection are derived with bias bounds and shown to be asymptotically unbiased for finite sequences without extrapolation.