87 citations · 160 across the 9 of their papers we have counts for
11 papers
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…
PRESTO: Predicting System-level Disruptions through Parametric Model Checking
Xinwei Fang, Radu Calinescu, Colin Paterson +1
Self-adaptive systems are expected to mitigate disruptions by continually adjusting their configuration and behaviour. This mitigation is often reactive. Typically, environmental o…
Quantitative Verification with Adaptive Uncertainty Reduction
Naif Alasmari, Radu Calinescu, Colin Paterson +1
Stochastic models are widely used to verify whether systems satisfy their reliability, performance and other nonfunctional requirements. However, the validity of the verification d…
Verified Synthesis of Optimal Safety Controllers for Human-Robot Collaboration
Mario Gleirscher, Radu Calinescu, James Douthwaite +5
We present a tool-supported approach for the synthesis, verification and validation of the control software responsible for the safety of the human-robot interaction in manufacturi…
Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
Danny Weyns, Bradley Schmerl, Masako Kishida +5
Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectura…
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers
Colin Paterson, Haoze Wu, John Grese +3
We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze…
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)
Richard Hawkins, Colin Paterson, Chiara Picardi +3
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains suc…
Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers
Colin Paterson, Radu Calinescu, Chiara Picardi
Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias…
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
Rob Ashmore, Radu Calinescu, Colin Paterson
Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed ma…
Efficient Parametric Model Checking Using Domain Knowledge
Radu Calinescu, Colin Paterson, Kenneth Johnson
We introduce an efficient parametric model checking (ePMC) method for the analysis of reliability, performance and other quality-of-service (QoS) properties of software systems. eP…
Observation-Enhanced QoS Analysis of Component-Based Systems
Colin Paterson, Radu Calinescu
We present a new method for the accurate analysis of the quality-of-service (QoS) properties of component-based systems. Our method takes as input a QoS property of interest and a…