2 papers
cs.AI2026
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
Ryohei Oura, Georgios Fainekos, Hideki Okamoto +1
Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur. Probability-…
eess.SY2025
Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guarantees
Ryohei Oura, Yuji Ito
Recent decision-making systems are increasingly complicated, making it crucial to verify and understand their behavior for a given specification. A promising approach is to compreh…