Attempts at combining logic and neural networks into neurosymbolic approaches have been
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …

We propose a novel, complete algorithm for the verification and analysis of feed-forward,
ReLU-based neural networks. The algorithm, based on symbolic interval propagation …

We study the interplay between counterfactual explanations and model multiplicity in the
context of neural network classifiers. We show that current explanation methods often …

We present a novel semidefinite programming (SDP) relaxation that enables tight and
efficient verification of neural networks. The tightness is achieved by combining SDP …

In this paper, we propose a system-level approach for verifying the safety of systems
combining a continuous-time physical system with a discrete-time neural network based …

Neural network approximations have become attractive to compress data for automation and
autonomy algorithms for use on storage-limited and processing-limited aerospace …

We present a novel and computationally efficient method for repairing a feed-forward neural
network with respect to a finite set of inputs that are misclassified. The method assumes no …

Formal verification of multi-agent systems is hard, both theoretically and in practice. In
particular, studies that use a single verification technique typically show limited efficiency …

Game-theoretic concepts have been extensively studied in economics to provide insight into
competitive behaviour and strategic decision making. As computing systems increasingly …

We present novel techniques for neuro-symbolic concurrent stochastic games, a recently
proposed modelling formalism to represent a set of probabilistic agents operating in a …