12 papers
Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification
Simos Gerasimou, Xingyu Zhao
Combining Bayesian learning and quantitative verification is a powerful toolset for analysing key quantitative properties of software systems, like reliability and response time. H…
Accelerating Policy Synthesis in Large-Scale MDPs via Hierarchical Adaptive Refinement
Alexandros Evangelidis, Gricel Vázquez, Simos Gerasimou
Software-intensive systems, such as software product lines and robotics, utilise Markov decision processes (MDPs) to capture uncertainty and analyse sequential decision-making prob…
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…
Formally Guaranteed Control Adaptation for ODD-Resilient Autonomous Systems
Gricel Vázquez, Calum Imrie, Sepeedeh Shahbeigi +5
Ensuring reliable performance in situations outside the Operational Design Domain (ODD) remains a primary challenge in devising resilient autonomous systems. We explore this challe…
Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
Charmaine Barker, Daniel Bethell, Simos Gerasimou
Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs may lead to detrimental outcomes. Eviden…
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:…