The 27th International Conference on Automation and Computing (ICAC2022)
1-3 Sep. 2022
Title: Developing Data-Driven Intelligent Autonomous Systems: information fusion; event correlation; decision making and planning
Abstract: Intelligent agents (and to some extent, intelligent robots) in data-driven intelligent autonomous systems, operating in large sensor networks with uncertain and dynamic environments, shall at least possess the following abilities. First, agents shall be able to perceive, analyze and combine uncertain and inconsistent information from multi-model heterogeneous sources in order to establish the true state of events that are of interest. Second, agents shall be able to model, correlate, and make inferences about dispersed events from different sources in real-time in order to achieve situation awareness. Third, agents shall be able to make decisions taking into account constraints and preferences, and to dynamically plan in order to act appropriately.
Over the past 20 years, my group, working together with UK and international collaborators, has been conducting research addressing these challenges. First, we developed numerous information fusion approaches to handle uncertain and inconsistent information under different circumstances. We have particularly established some common principles governing the fusion of both uncertain information (especially in numerical forms) and knowledge (especially in logical forms). Second, we developed several methods to correlate and reason with seemingly unrelated events to draw (high-level) conclusions that are beyond the immediate meaning of directly observed events. Third, we have been investigating how to integrate state-of-the-art planning algorithms with traditional agent architectures (such as Belief-Desire-Intension agents). Specifically, we have been looking into how to deploy First Principles Planners (FPPs), which are online planning algorithms, when an agent has no available plans to use. The ability for an agent to evolve its planning capabilities overtime has also been studied.
This talk will cover some of our major research results, with several demos (e.g., cyber-physical systems, smart grids) illustrating the potentials in real-world applications.
Biography: Weiru Liu holds Chair of Artificial Intelligence (AI) at the University of Bristol, and is Associate Dean for Temple Quarter Enterprise Campus (Research and Enterprise) since early 2020. Previously, she was the Faculty Research Director for the Engineering Faculty between 2017 to 2020. Her research interests include uncertain information fusion, event correlation and reasoning, large scale data analytics, agent systems with online planning, with a wide range of applications such as security, healthcare, robotics. Recently, she has been looking into approaches in explainable AI tailored to non-experts as part of an EPSRC funded project. She is also a Co-I of the £10m ESRC funded Centre for Sociodigital Futures examining social implications and the importance of social science in designing future AI technologies. She has published over 200 peer-reviewed papers, with several Best Paper Awards.She chaired a number of international conferences, and was an invited keynote speaker at several international conferences.
Prior to joining the University of Bristol in 2017, she held Chair of AI at Queen’s University Belfast, and was the Director of Research for the Knowledge and Data Engineering Cluster for 6 years.She has a sustained track record of securing peer-reviewed, highly competitive funding from a diverse range of funding bodies (over £58m as Principal Investigator or Co-Investigator), and was the PI for the £2.3m R&D grant funded by the Allstate Insurance Company (US) and Invest Northern Ireland on detecting fraudulence medical claims. In 2011, she received a Queen’s Impact Award (sponsored by EPSRC) at Queen’s University Belfast.
She has been a member of the UK EPSRC ICT Strategic Advisory Team (SAT) since 2017 and a member of UK Higher Education Research Excellence Framework (REF) 2021 Institutional-level Environment Pilot Panel (ILEPP). She was a member of Independent Research Fund Denmark (DFF), and a member of Academy of Finland AI and Data Science Review Panel. She is also a Co-Director of the Centre for Doctoral Training in Future Autonomous and Robotic Systems: Towards Ubiquity (FARSCOPE-TU) at Bristol.
Title: Smart Product and Service Ecosystem Design with Digital Twins
Abstract: In the context of Industry 4.0 technological development and digital transformation in industry and our society, this talk will first discuss the trends and challenges of shifting from product design to product-Service ecosystem (PSS) Design along the product lifecycle under the manufacturing servitisation. Secondly, it will discuss and demonstrate how digital twins and crowdsourcing technologies (e.g. Industry 4.0 technologies) are integrated for smart product service ecosystem design. To support this solution, a product design lifecycle information model (PDLIM) is then explored to potentially support data-driven design paradigm based on digital twins. Finally, it will discuss emerging directions of Human-in-the-loop machine learning or interactive machine learning for effective human-data (machine) co-decision making for digital-twin-based design approach and its challenges ahead.
Biography: Professor Shengfeng Qin is a professor of digital design and manufacturing in the school of Design, Northumbria University, UK.
Professor Qin joined Northumbria School of Design in 2014. He was 2019 Newton Prize recipient based on his collaborative research work with Professor Cuixia Ma at the Institute of Software of Chinese Academy of Sciences on Transforming Service Design and Big Data Technologies into Sustainable Urbanisation.
Prior to this appointment, he worked as a Senior/Lecturer in Department of Design at Brunel University (2001- 2013), a Post-Doctoral Research Fellow (2000-2001) at University of Loughborough, a Research Assistant in the School of Product Design of Cardiff Metropolitan University (1998-1999). He was an Academic Visitor at the University of Birmingham (1996-1997) from East China Jiaotong University.
His research interest is broadly in digital design and manufacturing methods and technology for product-service systems. Early work includes design optimization, Computer Aided Design and Manufacturing (CAD/CAM), sketch-based modelling and interfacing, gesture-based modelling and interfacing, design process and team management.
At Northumbria University, Professor Qin has established the Smart Design Lab (SDL), leading design research into future smart products, services and interconnected systems design by applying cutting edge smart technologies and smart multi-disciplinary design research methods/tools. He is also Co-director for Joint Design Innovation Lab between Northumbria University and Northwestern Polytechnical University of China.
Professor Qin is currently the Director for our MA Design Programme, teaching research principles and methods for this programme. He is the Editor-in-Chief for International Journal of Rapid manufacturing, and an Associate Editor for International Journal of Design Engineering.