6 papers
Certificates Synthesis for A Class of Observational Properties in Stochastic Systems: A Unified Approach
Bohan Cui, Jianing Zhao, Yu Chen +3
In this paper, we investigate the probabilistic formal verification of stochastic dynamical systems over continuous state spaces. Motivated by problems in state estimation and info…
About Time: Model-free Reinforcement Learning with Timed Reward Machines
Rajarshi Roy, Anirban Majumdar, Ritam Raha +2
Reward specification plays a central role in reinforcement learning (RL), guiding the agent's behavior. To express non-Markovian rewards, formalisms such as reward machines have be…
Learning Probabilistic Temporal Logic Specifications for Stochastic Systems
Rajarshi Roy, Yash Pote, David Parker +1
There has been substantial progress in the inference of formal behavioural specifications from sample trajectories, for example, using Linear Temporal Logic (LTL). However, these t…
Planning with Linear Temporal Logic Specifications: Handling Quantifiable and Unquantifiable Uncertainty
Pian Yu, Yong Li, David Parker +1
This work studies the planning problem for robotic systems under both quantifiable and unquantifiable uncertainty. The objective is to enable the robotic systems to optimally fulfi…
Safe POMDP Online Planning among Dynamic Agents via Adaptive Conformal Prediction
Shili Sheng, Pian Yu, David Parker +2
Online planning for partially observable Markov decision processes (POMDPs) provides efficient techniques for robot decision-making under uncertainty. However, existing methods fal…
Learning Algorithms for Verification of Markov Decision Processes
Tomáš Brázdil, Krishnendu Chatterjee, Martin Chmelik +6
We present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques…