32 citations · 80 across the 14 of their papers we have counts for
17 papers
Safe POMDP Online Planning via Shielding
Shili Sheng, David Parker, Lu Feng
Partially observable Markov decision processes (POMDPs) have been widely used in many robotic applications for sequential decision-making under uncertainty. POMDP online planning a…
Distributional Probabilistic Model Checking
Ingy Elsayed-Aly, David Parker, Lu Feng
Probabilistic model checking can provide formal guarantees on the behavior of stochastic models relating to a wide range of quantitative properties, such as runtime, energy consump…
Multi-Agent Verification and Control with Probabilistic Model Checking
David Parker
Probabilistic model checking is a technique for formal automated reasoning about software or hardware systems that operate in the context of uncertainty or stochasticity. It builds…
Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions
Thom Badings, Licio Romao, Alessandro Abate +4
Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynam…
Partially-Observable Security Games for Automating Attack-Defense Analysis
Narges Khakpour, David Parker
Network systems often contain vulnerabilities that remain unfixed in a network for various reasons, such as the lack of a patch or knowledge to fix them. With the presence of such…
Robust Anytime Learning of Markov Decision Processes
Marnix Suilen, Thiago D. Simão, David Parker +1
Markov decision processes (MDPs) are formal models commonly used in sequential decision-making. MDPs capture the stochasticity that may arise, for instance, from imprecise actuator…