87 citations · 160 across the 9 of their papers we have counts for
5 papers · 1 filter
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…