b'Nils Jansen'

research

∙ 05/13/2023

More for Less: Safe Policy Improvement With Stronger Performance Guarantees

In an offline reinforcement learning setting, the safe policy improvemen...

0 Patrick Wienhöft, et al. ∙

research

∙ 05/01/2023

Efficient Sensitivity Analysis for Parametric Robust Markov Chains

We provide a novel method for sensitivity analysis of parametric robust ...

0 Thom Badings, et al. ∙

research

∙ 03/14/2023

Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring

We study Markov decision processes (MDPs), where agents have direct cont...

0 Merlijn Krale, et al. ∙

research

∙ 03/10/2023

Decision-Making Under Uncertainty: Beyond Probabilities

This position paper reflects on the state-of-the-art in decision-making ...

0 Thom Badings, et al. ∙

research

∙ 01/26/2023

Robust Almost-Sure Reachability in Multi-Environment MDPs

Multiple-environment MDPs (MEMDPs) capture finite sets of MDPs that shar...

0 Marck van der Vegt, et al. ∙

research

∙ 01/12/2023

Safe Policy Improvement for POMDPs via Finite-State Controllers

We study safe policy improvement (SPI) for partially observable Markov d...

0 Thiago D. Simão, et al. ∙

research

∙ 01/04/2023

Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions

Controllers for dynamical systems that operate in safety-critical settin...

0 Thom Badings, et al. ∙

research

∙ 12/10/2022

Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking

Deep Reinforcement Learning (RL) agents are susceptible to adversarial n...

0 Dennis Gross, et al. ∙

research

∙ 12/01/2022

Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics

Automated synthesis of provably correct controllers for cyber-physical s...

0 Luke Rickard, et al. ∙

research

∙ 10/12/2022

Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty

Capturing uncertainty in models of complex dynamical systems is crucial ...

0 Thom Badings, et al. ∙

research

∙ 10/02/2022

Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation

We address the problem of safe reinforcement learning from pixel observa...

0 Yannick Hogewind, et al. ∙

research

∙ 09/15/2022

COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking

This paper presents COOL-MC, a tool that integrates state-of-the-art rei...

0 Dennis Gross, et al. ∙

research

∙ 08/01/2022

A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning

Cost-effective asset management is an area of interest across several in...

0 Zaharah A. Bukhsh, et al. ∙

research

∙ 07/14/2022

Parameter Synthesis in Markov Models: A Gentle Survey

This paper surveys the analysis of parametric Markov models whose transi...

0 Nils Jansen, et al. ∙

research

∙ 05/31/2022

Robust Anytime Learning of Markov Decision Processes

Markov decision processes (MDPs) are formal models commonly used in sequ...

0 Marnix Suilen, et al. ∙

research

∙ 05/17/2022

Sampling-Based Verification of CTMCs with Uncertain Rates

We employ uncertain parametric CTMCs with parametric transition rates an...

0 Thom S. Badings, et al. ∙

research

∙ 04/02/2022

Safe Reinforcement Learning via Shielding for POMDPs

Reinforcement learning (RL) in safety-critical environments requires an ...

0 Steven Carr, et al. ∙

research

∙ 12/24/2021

Scenario-Based Verification of Uncertain Parametric MDPs

We consider parametric Markov decision processes (pMDPs) that are augmen...

0 Thom S. Badings, et al. ∙

research

∙ 10/25/2021

Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise

Controllers for autonomous systems that operate in safety-critical setti...

0 Thom S. Badings, et al. ∙

research

∙ 06/30/2021

Convex Optimization for Parameter Synthesis in MDPs

Probabilistic model checking aims to prove whether a Markov decision pro...

0 Murat Cubuktepe, et al. ∙

research

∙ 05/27/2021

Model Repair Revamped: On the Automated Synthesis of Markov Chains

This paper outlines two approaches|based on counterexample-guided abstra...

0 Milan Ceska, et al. ∙

research

∙ 03/03/2021

Filter-Based Abstractions with Correctness Guarantees for Planning under Uncertainty

We study planning problems for continuous control systems with uncertain...

0 Thom S. Badings, et al. ∙

research

∙ 02/07/2021

Damage detection using in-domain and cross-domain transfer learning

We investigate the capabilities of transfer learning in the area of stru...

0 Zaharah A. Bukhsh, et al. ∙

research

∙ 10/08/2020

Adaptive Shielding under Uncertainty

This paper targets control problems that exhibit specific safety and per...

0 Stefan Pranger, et al. ∙

research

∙ 09/24/2020

Robust Finite-State Controllers for Uncertain POMDPs

Uncertain partially observable Markov decision processes (uPOMDPs) allow...

0 Murat Cubuktepe, et al. ∙

research

∙ 08/31/2020

Adversarial Patch Camouflage against Aerial Detection

Detection of military assets on the ground can be performed by applying ...

24 Ajaya Adhikari, et al. ∙

research

∙ 07/16/2020

Strengthening Deterministic Policies for POMDPs

The synthesis problem for partially observable Markov decision processes...

0 Leonore Winterer, et al. ∙

research

∙ 06/30/2020

Enforcing Almost-Sure Reachability in POMDPs

Partially-Observable Markov Decision Processes (POMDPs) are a well-known...

0 Sebastian Junges, et al. ∙

research

∙ 06/30/2020

Formalizing and Guaranteeing* Human-Robot Interaction

Robot capabilities are maturing across domains, from self-driving cars, ...

0 Hadas Kress-Gazit, et al. ∙

research

∙ 05/12/2020

Robustness Verification for Classifier Ensembles

We give a formal verification procedure that decides whether a classifie...

0 Dennis Gross, et al. ∙

research

∙ 02/13/2020

Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints

Recurrent neural networks (RNNs) have emerged as an effective representa...

0 Steven Carr, et al. ∙

research

∙ 12/24/2019

Scenario-Based Verification of Uncertain MDPs

We consider Markov decision processes (MDPs) in which the transition pro...

0 Murat Cubuktepe, et al. ∙

research

∙ 08/01/2019

Neural Simplex Architecture

We present the Neural Simplex Architecture (NSA), a new approach to runt...

0 Dung Phan, et al. ∙

research

∙ 05/15/2019

Synthesis of Provably Correct Autonomy Protocols for Shared Control

We synthesize shared control protocols subject to probabilistic temporal...

0 Murat Cubuktepe, et al. ∙

research

∙ 03/20/2019

Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks

We study strategy synthesis for partially observable Markov decision pro...

0 Steven Carr, et al. ∙

research

∙ 03/16/2019

Parameter Synthesis for Markov Models

Markov chain analysis is a key technique in reliability engineering. A p...

0 Sebastian Junges, et al. ∙

research

∙ 02/15/2019

Shepherding Hordes of Markov Chains

This paper considers large families of Markov chains (MCs) that are defi...

0 Milan Ceska, et al. ∙

research

∙ 09/28/2018

The Partially Observable Games We Play for Cyber Deception

Progressively intricate cyber infiltration mechanisms have made conventi...

0 Mohamadreza Ahmadi, et al. ∙

research

∙ 07/16/2018

Structured Synthesis for Probabilistic Systems

We introduce the concept of structured synthesis for Markov decision pro...

0 Nils Jansen, et al. ∙

research

∙ 07/16/2018

Shielded Decision-Making in MDPs

A prominent problem in artificial intelligence and machine learning is t...

0 Nils Jansen, et al. ∙

research

∙ 02/27/2018

Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes

We study planning problems where autonomous agents operate inside enviro...

0 Steven Carr, et al. ∙

research

∙ 10/24/2017

Permissive Finite-State Controllers of POMDPs using Parameter Synthesis

We study finite-state controllers (FSCs) for partially observable Markov...

0 Sebastian Junges, et al. ∙

research

∙ 08/14/2017

Motion Planning under Partial Observability using Game-Based Abstraction

We study motion planning problems where agents move inside environments ...

0 Leonore Winterer, et al. ∙

research

∙ 10/28/2016

Probabilistic Model Checking for Complex Cognitive Tasks -- A case study in human-robot interaction

This paper proposes to use probabilistic model checking to synthesize op...

0 Sebastian Junges, et al. ∙

research

∙ 10/26/2016

Synthesis of Shared Control Protocols with Provable Safety and Performance Guarantees

We formalize synthesis of shared control protocols with correctness guar...

0 Nils Jansen, et al. ∙

Nils Jansen

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