2 citations · 5 across the 14 of their papers we have counts for
14 papers
Adaptive Policy Portfolios for Robust Markov Decision Processes
Kasper Engelen, Sebastian Junges, Guillermo A. Pérez +1
Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unknown dynamics are fixed and become partia…
Fast Computation of Conditional Probabilities in MDPs and Markov Chain Families
Milan Češka, Sebastian Junges, Luko van der Maas +2
Computing optimal conditional reachability probabilities in Markov decision processes (MDPs) is tractable by a reduction to reachability probabilities. Yet, this reduction yields c…
Shields to Guarantee Probabilistic Safety in MDPs
Linus Heck, Filip Macák, Roman Andriushchenko +2
Shielding is a prominent model-based technique to ensure safety of autonomous agents. Classical shielding aims to ensure that nothing bad ever happens and comes with strong guarant…
Robust Finite-Memory Policy Gradients for Hidden-Model POMDPs
Maris F. L. Galesloot, Roman Andriushchenko, Milan Češka +2
Partially observable Markov decision processes (POMDPs) model specific environments in sequential decision-making under uncertainty. Critically, optimal policies for POMDPs may not…
Generalized Parameter Lifting: Finer Abstractions for Parametric Markov Chains
Linus Heck, Tim Quatmann, Jip Spel +2
Parametric Markov chains (pMCs) are Markov chains (MCs) with symbolic probabilities. A pMC encodes a family of MCs, where each member is obtained by replacing parameters with const…
Learning Verified Monitors for Hidden Markov Models
Luko van der Maas, Sebastian Junges
Runtime monitors assess whether a system is in an unsafe state based on a stream of observations. We study the problem where the system is subject to probabilistic uncertainty and…
Tighter Value-Function Approximations for POMDPs
Merlijn Krale, Wietze Koops, Sebastian Junges +2
Solving partially observable Markov decision processes (POMDPs) typically requires reasoning about the values of exponentially many state beliefs. Towards practical performance, st…
Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates
Thom Badings, Wietze Koops, Sebastian Junges +1
We consider the verification of neural network policies for discrete-time stochastic systems with respect to reach-avoid specifications. We use a learner-verifier procedure that le…
Tools at the Frontiers of Quantitative Verification
Roman Andriushchenko, Alexander Bork, Carlos E. Budde +20
The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool suppo…
Approximate Dec-POMDP Solving Using Multi-Agent A*
Wietze Koops, Sebastian Junges, Nils Jansen
We present an A*-based algorithm to compute policies for finite-horizon Dec-POMDPs. Our goal is to sacrifice optimality in favor of scalability for larger horizons. The main ingred…
Imprecise Probabilities Meet Partial Observability: Game Semantics for Robust POMDPs
Eline M. Bovy, Marnix Suilen, Sebastian Junges +1
Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concer…
CTMCs with Imprecisely Timed Observations
Thom Badings, Matthias Volk, Sebastian Junges +2
Labeled continuous-time Markov chains (CTMCs) describe processes subject to random timing and partial observability. In applications such as runtime monitoring, we must incorporate…
Factored Online Planning in Many-Agent POMDPs
Maris F. L. Galesloot, Thiago D. Simão, Sebastian Junges +1
In centralized multi-agent systems, often modeled as multi-agent partially observable Markov decision processes (MPOMDPs), the action and observation spaces grow exponentially with…
Strategy Synthesis in POMDPs via Game-Based Abstractions
Leonore Winterer, Sebastian Junges, Ralf Wimmer +4
We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guarantee…