IsaGrad paper accepted at LogicNN 2026
Our paper “IsaGrad: Verified Automatic Differentiation over Computational Graphs in Imperative HOL” has been accepted and will be presented at LogicNN, FLOC 2026.
formal verification
Our paper on understanding the ADL of older adults has been published by IEEE Sensors
Our paper describing “A Personalised Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes” has been published by the journal IEEE Sensors. This describes a novel approach and framework integrating symbolic modelling and data from sensors for understanding the Activities of Daily Living (ADLs) of older adults living independently in their homes in Edinburgh and its neighbourhood.
The work is part of the Integrated Technologies of Care workpackage of the Advanced Care Research Centre (ACRC).
Abstract:
There is an urgent need to provide quality-of-life to a growing population of older adults living independently. Solutions that focus on the person and take into account their preferences and context are recognised as key. We introduce a framework for representing and reasoning about the Activities of Daily Living of older adults living independently at home. The framework integrates data from sensors and data from participants derived from semi-structured interviews, home layouts and additional contextual information, such as the researchers’ observations. These data are used to create formal models, personalised for each participant according to their preferences and context. Requirements specific to each individual are formulated and encoded in Linear Temporal Logic, and a model checker is used to verify whether each is satisfied by the model of the participant’s behaviour. We demonstrate the framework’s generalisability by applying it to two different participants, highlighting its potential to enhance the safety and well-being of older adults ageing in place.
Print ISSN: 1530-437X
Online ISSN: 1558-1748
DOI: 10.1109/JSEN.2025.3635781
More information available here.
Our preprint on formally verified neurosymbolic trajectory learning is out on arXiv
Formally Verified Neurosymbolic Trajectory Learning via Tensor-based Linear Temporal Logic on Finite Traces
Astract:
We present a novel formalisation of tensor semantics for linear temporal logic on finite traces (LTLf), with formal proofs of correctness carried out in the theorem prover Isabelle/HOL. We demonstrate that this formalisation can be integrated into a neurosymbolic learning process by defining and verifying a differentiable loss function for the LTLf constraints, and automatically generating an implementation that integrates with PyTorch. We show that, by using this loss, the process learns to satisfy pre-specified logical constraints. Our approach offers a fully rigorous framework for constrained training, eliminating many of the inherent risks of ad-hoc, manual implementations of logical aspects directly in an “unsafe” programming language such as Python, while retaining efficiency in implementation.
Our formalisation of Linear Resources and Process Compositions has been published in the Archive of Formal Proof
Abstract
In this entry we formalise a framework for process composition based on actions that are specified by their input and output resources. We verify their correctness by translating compositions of process into deductions of intuitionistic linear logic. As part of the verification we derive simple conditions on the compositions which ensure well-formedness of the corresponding deduction.
We describe an earlier version of this formalisation in our article Linear Resources in Isabelle/HOL, which also includes a formalisation of manufacturing processes in the simulation game Factorio.
Our paper “Linear Resources in Isabelle/HOL” has just been published in the Journal of Automated Reasoning
Abstract:
We present a formal framework for process composition based on actions that are specified by their input and output resources. The correctness of these compositions is verified by translating them into deductions in intuitionistic linear logic. As part of the verification we derive simple conditions on the compositions which ensure well-formedness of the corresponding deduction when satisfied. We mechanise the whole framework, including a deep embedding of ILL, in the proof assistant Isabelle/HOL. Beyond the increased confidence in our proofs, this allows us to automatically generate executable code for our verified definitions. We demonstrate our approach by formalising part of the simulation game Factorio and modelling a manufacturing process in it. Our framework guarantees that this model is free of bottlenecks.
Smola, F., Fleuriot, J.D. Linear Resources in Isabelle/HOL. J Autom Reasoning 68, 9 (2024). https://doi.org/10.1007/s10817-024-09698-2
Mark Chevallier passes his second year PhD review
Mark successfully passed his second year PhD review on formal verification applied to machine learning. His panel consisted of Pavlos Andreadis, Paul Jackson and Jacques Fleuriot. Congratulations to Mark!