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.
Basic books
4 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
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.
A workshop report catalogues challenges and solution pathways for verification, engineering, and architecting reliable autonomous systems.
citing papers explorer
-
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.