Alessandro Abate

research

∙ 09/12/2023

A General Verification Framework for Dynamical and Control Models via Certificate Synthesis

An emerging branch of control theory specialises in certificate learning...

0 Alec Edwards, et al. ∙

research

∙ 07/28/2023

On the Trade-off Between Efficiency and Precision of Neural Abstraction

Neural abstractions have been recently introduced as formal approximatio...

0 Alec Edwards, et al. ∙

research

∙ 07/11/2023

On Imperfect Recall in Multi-Agent Influence Diagrams

Multi-agent influence diagrams (MAIDs) are a popular game-theoretic mode...

0 James Fox, et al. ∙

research

∙ 06/05/2023

Networked Communication for Decentralised Agents in Mean-Field Games

We introduce networked communication to the mean-field game framework. I...

0 Patrick Benjamin, et al. ∙

research

∙ 03/30/2023

Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]

We introduce an adaptive refinement procedure for smart, and scalable ab...

0 Adrien Banse, et al. ∙

research

∙ 03/23/2023

Policy Evaluation in Distributional LQR

Distributional reinforcement learning (DRL) enhances the understanding o...

0 Zifan Wang, et al. ∙

research

∙ 02/27/2023

k-Prize Weighted Voting Games

We introduce a natural variant of weighted voting games, which we refer ...

0 Wei-Chen Lee, et al. ∙

research

∙ 01/27/2023

Neural Abstractions

We present a novel method for the safety verification of nonlinear dynam...

0 Alessandro Abate, et al. ∙

research

∙ 01/15/2023

Quantitative Verification With Neural Networks For Probabilistic Programs and Stochastic Systems

We present a machine learning approach to quantitative verification. We ...

0 Alessandro Abate, et al. ∙

research

∙ 01/05/2023

Reasoning about Causality in Games

Causal reasoning and game-theoretic reasoning are fundamental topics in ...

0 Lewis Hammond, 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

∙ 12/28/2022

Lexicographic Multi-Objective Reinforcement Learning

In this work we introduce reinforcement learning techniques for solving ...

0 Joar Skalse, et al. ∙

research

∙ 12/06/2022

Misspecification in Inverse Reinforcement Learning

The aim of Inverse Reinforcement Learning (IRL) is to infer a reward fun...

0 Joar Skalse, 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

∙ 09/30/2022

Observational Robustness and Invariances in Reinforcement Learning via Lexicographic Objectives

Policy robustness in Reinforcement Learning (RL) may not be desirable at...

0 Daniel Jarne Ornia, et al. ∙

research

∙ 09/21/2022

LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learning

LCRL is a software tool that implements model-free Reinforcement Learnin...

0 Hosein Hasanbeig, et al. ∙

research

∙ 08/25/2022

Learning Task Automata for Reinforcement Learning using Hidden Markov Models

Training reinforcement learning (RL) agents using scalar reward signals ...

5 Alessandro Abate, et al. ∙

research

∙ 08/12/2022

Low Emission Building Control with Zero-Shot Reinforcement Learning

Heating and cooling systems in buildings account for 31% of global energ...

0 Scott R. Jeen, et al. ∙

research

∙ 03/14/2022

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning

It's challenging to design reward functions for complex, real-world task...

0 Joar Skalse, 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

∙ 05/21/2021

Certification of Iterative Predictions in Bayesian Neural Networks

We consider the problem of computing reach-avoid probabilities for itera...

7 Matthew Wicker, et al. ∙

research

∙ 04/29/2021

Grid-Free Computation of Probabilistic Safety with Malliavin Calculus

We work with continuous-time, continuous-space stochastic dynamical syst...

0 Francesco Cosentino, et al. ∙

research

∙ 02/24/2021

Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic

This paper investigates the motion planning of autonomous dynamical syst...

27 Mingyu Cai, et al. ∙

research

∙ 02/09/2021

Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice

Multi-agent influence diagrams (MAIDs) are a popular form of graphical m...

18 Lewis Hammond, et al. ∙

research

∙ 02/01/2021

Multi-Agent Reinforcement Learning with Temporal Logic Specifications

In this paper, we study the problem of learning to satisfy temporal logi...

11 Lewis Hammond, et al. ∙

research

∙ 01/19/2021

Automated Verification and Synthesis of Stochastic Hybrid Systems: A Survey

Stochastic hybrid systems have received significant attentions as a rele...

0 Abolfazl Lavaei, et al. ∙

research

∙ 08/07/2020

SafePILCO: a software tool for safe and data-efficient policy synthesis

SafePILCO is a software tool for safe and data-efficient policy search w...

0 Kyriakos Polymenakos, et al. ∙

research

∙ 07/21/2020

Automated and Sound Synthesis of Lyapunov Functions with SMT Solvers

In this paper we employ SMT solvers to soundly synthesise Lyapunov funct...

0 Daniele Ahmed, et al. ∙

research

∙ 07/09/2020

Symbolic Reachability Analysis of High Dimensional Max-Plus Linear Systems

This work discusses the reachability analysis (RA) of Max-Plus Linear (M...

0 Muhammad Syifa'ul Mufid, et al. ∙

research

∙ 07/07/2020

Automated Formal Synthesis of Neural Barrier Certificates for Dynamical Models

We introduce an automated, formal, counterexample-based approach to synt...

0 Andrea Peruffo, et al. ∙

research

∙ 07/06/2020

Jump Operator Planning: Goal-Conditioned Policy Ensembles and Zero-Shot Transfer

In Hierarchical Control, compositionality, abstraction, and task-transfe...

0 Thomas J. Ringstrom, et al. ∙

research

∙ 07/01/2020

Computation of the Transient in Max-Plus Linear Systems via SMT-Solving

This paper proposes a new approach, grounded in Satisfiability Modulo Th...

0 Alessandro Abate, et al. ∙

research

∙ 06/02/2020

Acceleration of Descent-based Optimization Algorithms via Carathéodory's Theorem

We propose a new technique to accelerate algorithms based on Gradient De...

0 Francesco Cosentino, et al. ∙

research

∙ 06/02/2020

A Randomized Algorithm to Reduce the Support of Discrete Measures

Given a discrete probability measure supported on N atoms and a set of n...

0 Francesco Cosentino, et al. ∙

research

∙ 04/23/2020

Bayesian Verification of Chemical Reaction Networks

We present a data-driven verification approach that determines whether o...

0 Gareth W. Molyneux, et al. ∙

research

∙ 03/19/2020

Automated Formal Synthesis of Lyapunov Neural Networks

We propose an automated and sound technique to synthesize provably corre...

0 Alessandro Abate, et al. ∙

research

∙ 02/26/2020

Cautious Reinforcement Learning with Logical Constraints

This paper presents the concept of an adaptive safe padding that forces ...

0 Mohammadhosein Hasanbeig, et al. ∙

research

∙ 11/29/2019

Safety Guarantees for Planning Based on Iterative Gaussian Processes

Gaussian Processes (GPs) are widely employed in control and learning bec...

13 Kyriakos Polymenakos, et al. ∙

research

∙ 11/22/2019

DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

We propose a method for effective training of deep Reinforcement Learnin...

0 Mohammadhosein Hasanbeig, et al. ∙

research

∙ 11/22/2019

DeepSynth: Program Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

We propose a method for efficient training of deep Reinforcement Learnin...

0 Mohammadhosein Hasanbeig, et al. ∙

research

∙ 09/23/2019

Modular Deep Reinforcement Learning with Temporal Logic Specifications

We propose an actor-critic, model-free, and online Reinforcement Learnin...

0 Lim Zun Yuan, et al. ∙

research

∙ 09/17/2019

Verifying Reachability Properties in Markov Chains via Incremental Induction

There is a scalability gap between probabilistic and non-probabilistic v...

0 Elizabeth Polgreen, et al. ∙

research

∙ 09/11/2019

Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees

Reinforcement Learning (RL) has emerged as an efficient method of choice...

8 Mohammadhosein Hasanbeig, et al. ∙

research

∙ 07/08/2019

Bounded Model Checking of Max-Plus Linear Systems via Predicate Abstractions

This paper introduces the abstraction of max-plus linear (MPL) systems v...

0 Muhammad Syifa'ul Mufid, et al. ∙

research

∙ 02/02/2019

Certified Reinforcement Learning with Logic Guidance

This paper proposes the first model-free Reinforcement Learning (RL) fra...

12 Mohammadhosein Hasanbeig, et al. ∙

research

∙ 09/20/2018

Logically-Constrained Neural Fitted Q-Iteration

This paper proposes a method for efficient training of the Q-function fo...

6 Mohammadhosein Hasanbeig, et al. ∙

research

∙ 01/24/2018

Logically-Correct Reinforcement Learning

We propose a novel Reinforcement Learning (RL) algorithm to synthesize p...

0 Mohammadhosein Hasanbeig, et al. ∙

research

∙ 01/12/2018

Efficient Probabilistic Model Checking of Smart Building Maintenance using Fault Maintenance Trees

Cyber-physical systems, like Smart Buildings and power plants, have to m...

0 Nathalie Cauchi, et al. ∙

research

∙ 12/20/2017

Temporal logic control of general Markov decision processes by approximate policy refinement

The formal verification and controller synthesis for Markov decision pro...

0 Sofie Haesaert, et al. ∙

Alessandro Abate

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