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The DEIS Research Group at the University of Hull is at the forefront of pioneering work in Dependable Intelligent Systems — advanced intelligent systems whose failure could pose serious risks to people, the environment, the economy, and society.
These systems span a wide range of applications, including autonomous vehicles, multi-robot systems in healthcare and industrial automation, drone swarms for infrastructure maintenance and logistics, as well as machine learning algorithms and generative AI supporting a diverse set of tasks.
The inherent Complexity, Intelligence, Autonomy, and Openness of these systems — and broader Systems of Systems (SoS) — introduce substantial challenges for ensuring Dependability, i.e. their safety, reliability, security, and privacy. For over 25 years, the DEIS group has been developing cutting-edge methods and tools to assess and assure the dependability of intelligent systems.
Among our key technologies are HiP-HOPS (Hierarchically Performed Hazard Origin and Propagation Studies) and SafeML (Safety of Machine Learning), which are already commercialised or in the process of commercialisation. These tools are used by leading global organisations such as Honda, Nissan, and NYK Lines in Japan; Huawei in China; Honeywell in the US; Volvo in Sweden; Continental in Germany; Fiat in Italy; and Embraer in Brazil.
The group is led by Professor Yiannis Papadopoulos and co-led by Dr Koorosh Aslansefat.
Our Research Focus
Dependability is a broad system property that encompasses attributes such as reliability and safety. Reliability refers to a system’s ability to function continuously without failure, while safety concerns its ability to avoid hazardous failures that could cause harm to people or the environment.
Traditionally, dependability analysis involves:
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Identifying hazards associated with the system under development,
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Analyzing potential causes of those hazards, and
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Improving the design to reduce risk to acceptable levels.​
In current industrial practice, this analysis is often carried out manually using well-established techniques such as Fault Tree Analysis. It typically relies on the experience and insight of engineers and analysts, and although increasingly supported by system models, testing, and simulation, the process remains largely imagination-driven.
However, the emergence of a new generation of complex, autonomous, intelligent, and connected systems and Systems of Systems (SoS) presents a major disruption to this approach.
Examples include:
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Surgical robots and robotic caregivers,
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Autonomous vehicles,
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Internet-connected factories,
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Real-time financial trading algorithms,
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Mobile factory robots, and
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Drone fleets delivering goods to our homes.​
These systems are too complex and dynamic to be adequately analyzed using traditional, manual techniques. To frame the challenges they pose for dependability, we use the acronym
Unprecedented Velocity. Impeccable Reliability.
At the DEIS Research Group at the University of Hull, we are leading cutting-edge research to tackle these issues. Our technologies are already making global impact, with tools either commercialised or in the process of commercialisation and licensed by major industrial players—from Honda and Toyota in Japan, to Huawei in China, Honeywell in the US, Volvo in Sweden, Continental in Germany, Fiat in Italy, and Embraer in Brazil.
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