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07/26/2023
Reinforcement Learning by Guided Safe Exploration
Safety is critical to broadening the application of reinforcement learni...
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05/13/2023
More for Less: Safe Policy Improvement With Stronger Performance Guarantees
In an offline reinforcement learning setting, the safe policy improvemen...
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05/01/2023
Efficient Sensitivity Analysis for Parametric Robust Markov Chains
We provide a novel method for sensitivity analysis of parametric robust ...
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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...
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03/10/2023
Decision-Making Under Uncertainty: Beyond Probabilities
This position paper reflects on the state-of-the-art in decision-making ...
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01/26/2023
Robust Almost-Sure Reachability in Multi-Environment MDPs
Multiple-environment MDPs (MEMDPs) capture finite sets of MDPs that shar...
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01/12/2023
Safe Policy Improvement for POMDPs via Finite-State Controllers
We study safe policy improvement (SPI) for partially observable Markov d...
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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...
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12/10/2022
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking
Deep Reinforcement Learning (RL) agents are susceptible to adversarial n...
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12/01/2022
Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics
Automated synthesis of provably correct controllers for cyber-physical s...
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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 ...
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10/02/2022
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
We address the problem of safe reinforcement learning from pixel observa...
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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...
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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...
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07/14/2022
Parameter Synthesis in Markov Models: A Gentle Survey
This paper surveys the analysis of parametric Markov models whose transi...
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05/31/2022
Robust Anytime Learning of Markov Decision Processes
Markov decision processes (MDPs) are formal models commonly used in sequ...
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05/17/2022
Sampling-Based Verification of CTMCs with Uncertain Rates
We employ uncertain parametric CTMCs with parametric transition rates an...
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04/02/2022
Safe Reinforcement Learning via Shielding for POMDPs
Reinforcement learning (RL) in safety-critical environments requires an ...
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12/24/2021
Scenario-Based Verification of Uncertain Parametric MDPs
We consider parametric Markov decision processes (pMDPs) that are augmen...
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10/25/2021
Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise
Controllers for autonomous systems that operate in safety-critical setti...
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06/30/2021
Convex Optimization for Parameter Synthesis in MDPs
Probabilistic model checking aims to prove whether a Markov decision pro...
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05/27/2021
Model Repair Revamped: On the Automated Synthesis of Markov Chains
This paper outlines two approaches|based on counterexample-guided abstra...
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03/03/2021
Filter-Based Abstractions with Correctness Guarantees for Planning under Uncertainty
We study planning problems for continuous control systems with uncertain...
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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...
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10/08/2020
Adaptive Shielding under Uncertainty
This paper targets control problems that exhibit specific safety and per...
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09/24/2020
Robust Finite-State Controllers for Uncertain POMDPs
Uncertain partially observable Markov decision processes (uPOMDPs) allow...
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08/31/2020
Adversarial Patch Camouflage against Aerial Detection
Detection of military assets on the ground can be performed by applying ...
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07/16/2020
Strengthening Deterministic Policies for POMDPs
The synthesis problem for partially observable Markov decision processes...
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06/30/2020
Enforcing Almost-Sure Reachability in POMDPs
Partially-Observable Markov Decision Processes (POMDPs) are a well-known...
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06/30/2020
Formalizing and Guaranteeing* Human-Robot Interaction
Robot capabilities are maturing across domains, from self-driving cars, ...
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05/12/2020
Robustness Verification for Classifier Ensembles
We give a formal verification procedure that decides whether a classifie...
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02/13/2020
Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints
Recurrent neural networks (RNNs) have emerged as an effective representa...
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12/24/2019
Scenario-Based Verification of Uncertain MDPs
We consider Markov decision processes (MDPs) in which the transition pro...
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08/01/2019
Neural Simplex Architecture
We present the Neural Simplex Architecture (NSA), a new approach to runt...
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05/15/2019
Synthesis of Provably Correct Autonomy Protocols for Shared Control
We synthesize shared control protocols subject to probabilistic temporal...
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03/20/2019
Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks
We study strategy synthesis for partially observable Markov decision pro...
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03/16/2019
Parameter Synthesis for Markov Models
Markov chain analysis is a key technique in reliability engineering. A p...
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02/15/2019
Shepherding Hordes of Markov Chains
This paper considers large families of Markov chains (MCs) that are defi...
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09/28/2018
The Partially Observable Games We Play for Cyber Deception
Progressively intricate cyber infiltration mechanisms have made conventi...
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07/16/2018
Structured Synthesis for Probabilistic Systems
We introduce the concept of structured synthesis for Markov decision pro...
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07/16/2018
Shielded Decision-Making in MDPs
A prominent problem in artificial intelligence and machine learning is t...
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02/27/2018
Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes
We study planning problems where autonomous agents operate inside enviro...
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10/24/2017
Permissive Finite-State Controllers of POMDPs using Parameter Synthesis
We study finite-state controllers (FSCs) for partially observable Markov...
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08/14/2017
Motion Planning under Partial Observability using Game-Based Abstraction
We study motion planning problems where agents move inside environments ...
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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...
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10/26/2016