In autonomous and multi-agent systems players are normally assumed rational and cooperating or competing in groups to achieve their overall objectives. Useful methods to study the resulting interactions come from game theory whereby notions such as cooperative games,...
Considerable work has been carried out in the past two decades on Verification of Multi-Agent Systems. Various methods based on binary-decision diagrams, bounded model checking, abstraction, symmetry reduction have been developed. Model checkers such as MCMAS and...
Machine learning (ML) techniques such as Support Vector Machines, Random Forests and Neural Networks are being applied with great success to a wide range of complex and sometimes safety-critical tasks. Recent research in ‘adversarial machine learning’ has...
AI is continuing to make progress in many settings, fuelled by data availability and computational power, but it is widely acknowledged that it cannot fully benefit society without addressing its widespread inability to explain its outputs, causing human mistrust....
When using complex algorithms to make decisions within autonomous systems, the weak link is the abstract model used by the algorithms: any errors in the model may lead to unanticipated behaviour potentially risking successful task completion, human safety, or legal...
“The extraction of (symbolic) rules which describe the operation of (deep) neural networks which have been trained to perform a certain task is central to explaining their inner workings in order to judge their correctness, bias-freeness, etc. Besides this it...
