Completed Engineering Computing & AI

UKRI Trustworthy Autonomous Systems Node in Resilience

In plain English

AI plain-English summary

Autonomous systems that maintain city infrastructure, diagnose disease, or manage traffic will need to handle unexpected failures without breaking social and legal rules. This project builds a mathematical toolbox to give them that capability. Current autonomous systems are brittle. They cannot adapt when sensors fail, environments change, or unexpected events occur. Even if they had perfect hardware, they lack the ability to weigh technical requirements against social, legal, ethical, and cultural norms while recovering from disruption. The gap is not in components but in how systems reason about trade-offs under pressure. If successful, this work will give developers formal notations and methods to design autonomous systems that anticipate faults, cooperate with humans and other machines, and provide clear evidence that their decisions comply with both technical and societal rules. The immediate impact is on infrastructure, road traffic, and medical diagnostics—systems where failure has high consequences and where trust depends on transparency. The research is applied and multidisciplinary, drawing on computer science, engineering, psychology, philosophy, and law to produce a practical toolkit for building resilient, trustworthy autonomous systems.

View original technical description
Imagine a future where autonomous systems are widely available to improve our lives. In this future, autonomous robots unobtrusively maintain the infrastructure of our cities, and support people in living fulfilled independent lives. In this future, autonomous software reliably diagnoses disease at early stages, and dependably manages our road traffic to maximise flow and minimise environmental impact. Before this vision becomes reality, several major limitations of current autonomous systems need to be addressed. Key among these limitations is their reduced resilience: today's autonomous systems cannot avoid, withstand, recover from, adapt, and evolve to handle the uncertainty, change, faults, failure, adversity, and other disruptions present in such applications. Recent and forthcoming technological advances will provide autonomous systems with many of the sensors, actuators and other functional building blocks required to achieve the desired resilience levels, but this is not enough. To be resilient and trustworthy in these important applications, future autonomous systems will also need to use these building blocks effectively, so that they achieve complex technical requirements without violating our social, legal, ethical, empathy and cultural (SLEEC) rules and norms. Additionally, they will need to provide us with compelling evidence that the decisions and actions supporting their resilience satisfy both technical and SLEEC-compliance goals. To address these challenging needs, our project will develop a comprehensive toolbox of mathematically based notations and models, SLEEC-compliant resilience-enhancing methods, and systematic approaches for developing, deploying, optimising, and assuring highly resilient autonomous systems and systems of systems. To this end, we will capture the multidisciplinary nature of the social and technical aspects of the environment in which autonomous systems operate - and of the systems themselves - via mathematical models. For that, we have a team of Computer Scientists, Engineers, Psychologists, Philosophers, Lawyers, and Mathematicians, with an extensive track record of delivering research in all areas of the project. Working with such a mathematical model, autonomous systems will determine which resilience- enhancing actions are feasible, meet technical requirements, and are compliant with the relevant SLEEC rules and norms. Like humans, our autonomous systems will be able to reduce uncertainty, and to predict, detect and respond to change, faults, failures and adversity, proactively and efficiently. Like humans, if needed, our autonomous systems will share knowledge and services with humans and other autonomous agents. Like humans, if needed, our autonomous systems will cooperate with one another and with humans, and will proactively seek assistance from experts. Our work will deliver a step change in developing resilient autonomous systems and systems of systems. Developers will have notations and guidance to specify the socio-technical norms and rules applicable to the operational context of their autonomous systems, and techniques to design resilient autonomous systems that are trustworthy and compliant with these norms and rules. Additionally, developers will have guidance to build autonomous systems that can tolerate disruption, making the system usable in a larger set of circumstances. Finally, they will have techniques to develop resilient autonomous systems that can share information and services with peer systems and humans, and methods for providing evidence of the resilience of their systems. In such a context, autonomous systems and systems of systems will be highly resilient and trustworthy.

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Researchers

Alan Price Thomas (Co-Investigator)Amel Bennaceur (Co-Investigator)Ana Cavalcanti (Co-Investigator)Arvind Thiruvallore Thattai (Co-Investigator)Bashar Nuseibeh (Co-Investigator)Ibrahim Habli (Co-Investigator)James Law (Co-Investigator)Julie Wilson (Co-Investigator)Katherine Plant (Co-Investigator)Lyudmila Mihaylova (Co-Investigator)Mark Levine (Co-Investigator)Neville Stanton (Co-Investigator)Radu Calinescu (Principal Investigator)Sanja Dogramadzi (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

UKRI Trustworthy Autonomous Systems Node in Functionality
UKRI Trustworthy Autonomous Systems Node in Verifiability
Trusted Autonomous Systems
Engineering for Cyber Resilience: Through-Life Modelling and Analysis (ENCYRCLE)
Socio-technical resilience in software development (STRIDE)

Original classification

Research Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.