87 citations · 294 across the 41 of their papers we have counts for
45 papers
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
Gricel Vázquez, Alexandros Evangelidis, Sepeedeh Shahbeigi +2
Integrating Large Language Models (LLMs) into complex software systems enables the generation of human-understandable explanations of opaque AI processes, such as automated task pl…
Hazard Management in Robot-Assisted Mammography Support
Ioannis Stefanakos, Roisin Bradley, Radu Calinescu +3
Robotic and embodied-AI systems have the potential to improve accessibility and quality of care in clinical settings, but their deployment in close physical contact with vulnerable…
Social, Legal, Ethical, Empathetic and Cultural Norm Operationalisation for AI Agents
Radu Calinescu, Ana Cavalcanti, Marsha Chechik +2
As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC…
Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing
Kavan Fatehi, Mostafa Rahmani Ghourtani, Amir Sonee +4
Sixth-generation (6G) radio access networks (RANs) must enforce strict service-level agreements (SLAs) for heterogeneous slices, yet sudden latency spikes remain difficult to diagn…
Automated Formalization of Probabilistic Requirements from Structured Natural Language
Anastasia Mavridou, Marie Farrell, Gricel Vázquez +4
Integrating autonomous and adaptive behavior into software-intensive systems presents significant challenges for software development, as uncertainties in the environment or decisi…
Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Reliable navigation in safety-critical environments requires both accurate hazard perception and principled uncertainty handling to strengthen downstream safety handling. Despite t…
Safe But Not Sorry: Reducing Over-Conservatism in Safety Critics via Uncertainty-Aware Modulation
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Ensuring the safe exploration of reinforcement learning (RL) agents is critical for deployment in real-world systems. Yet existing approaches struggle to strike the right balance:…
Conformal Safety Shielding for Imperfect-Perception Agents
William Scarbro, Calum Imrie, Sinem Getir Yaman +4
We consider the problem of safe control in discrete autonomous agents that use learned components for imperfect perception (or more generally, state estimation) from high-dimension…
Tool for Supporting Debugging and Understanding of Normative Requirements Using LLMs
Alex Kleijwegt, Sinem Getir Yaman, Radu Calinescu
Normative requirements specify social, legal, ethical, empathetic, and cultural (SLEEC) norms that must be observed by a system. To support the identification of SLEEC requirements…
Symbolic Runtime Verification and Adaptive Decision-Making for Robot-Assisted Dressing
Yasmin Rafiq, Gricel Vázquez, Radu Calinescu +2
We present a control framework for robot-assisted dressing that augments low-level hazard response with runtime monitoring and formal verification. A parametric discrete-time Marko…
Verification and External Parameter Inference for Stochastic World Models
Radu Calinescu, Sinem Getir Yaman, Simos Gerasimou +2
Given its ability to analyse stochastic models ranging from discrete and continuous-time Markov chains to Markov decision processes and stochastic games, probabilistic model checki…
Normative Requirements Operationalization with Large Language Models
Nick Feng, Lina Marsso, S. Getir Yaman +9
Normative non-functional requirements specify constraints that a system must observe in order to avoid violations of social, legal, ethical, empathetic, and cultural norms. As thes…
Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
Daniel Bethell, Simos Gerasimou, Radu Calinescu +1
Empowering safe exploration of reinforcement learning (RL) agents during training is a critical challenge towards their deployment in many real-world scenarios. When prior knowledg…
Formal Synthesis of Uncertainty Reduction Controllers
Marc Carwehl, Calum Imrie, Thomas Vogel +3
In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behavio…
Analyzing and Debugging Normative Requirements via Satisfiability Checking
Nick Feng, Lina Marsso, Sinem Getir Yaman +11
As software systems increasingly interact with humans in application domains such as transportation and healthcare, they raise concerns related to the social, legal, ethical, empat…
Out-of-distribution Object Detection through Bayesian Uncertainty Estimation
Tianhao Zhang, Shenglin Wang, Nidhal Bouaynaya +2
The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many prac…
Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
Daniel Bethell, Simos Gerasimou, Radu Calinescu
Deploying deep learning models in safety-critical applications remains a very challenging task, mandating the provision of assurances for the dependable operation of these models.…
Specification, Validation and Verification of Social, Legal, Ethical, Empathetic and Cultural Requirements for Autonomous Agents
Sinem Getir Yaman, Ana Cavalcanti, Radu Calinescu +3
Autonomous agents are increasingly being proposed for use in healthcare, assistive care, education, and other applications governed by complex human-centric norms. To ensure compli…
Bayesian Learning for the Robust Verification of Autonomous Robots
Xingyu Zhao, Simos Gerasimou, Radu Calinescu +3
Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify the…
Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study
Corina S. Pasareanu, Ravi Mangal, Divya Gopinath +4
Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these sy…
Specification Architectural Viewpoint for Benefit-Cost-Risk-Aware Decision-Making in Self-Adaptive Systems
Danny Weyns, Paris Avegriou, Radu Calinescu +3
Over the past two decades, researchers and engineers have extensively studied the problem of how to enable a software system to deal with uncertain operating conditions. One promin…
Towards Adaptive Planning of Assistive-care Robot Tasks
Jordan Hamilton, Ioannis Stefanakos, Radu Calinescu +1
This 'research preview' paper introduces an adaptive path planning framework for robotic mission execution in assistive-care applications. The framework provides a graph-based envi…
Scheduling of Missions with Constrained Tasks for Heterogeneous Robot Systems
Gricel Vázquez, Radu Calinescu, Javier Cámara
We present a formal tasK AllocatioN and scheduling apprOAch for multi-robot missions (KANOA). KANOA supports two important types of task constraints: task ordering, which requires…
Software Performability Analysis Using Fast Parametric Model Checking
Xinwei Fang, Radu Calinescu, Simos Gerasimou +1
We present an efficient parametric model checking (PMC) technique for the analysis of software performability, i.e., of the performance and dependability properties of software sys…