Over the past decade, researchers in Artificial Intelligence (AI) have made ground-breaking progress on long-standing problems. Now that AI is becoming increasingly part of our daily lives, we need to avoid being ruled by machines and their decisions. Hybrid Intelligence (HI) is the combination of human and machine intelligence, expanding human intellect instead of replacing it. It takes human expertise and intentionality into account when making meaningful decisions and perform appropriate actions, together with ethical, legal and societal values. Our goal is to design Hybrid Intelligent systems, an approach to Artificial Intelligence that puts humans at the center, changing the course of the ongoing AI revolution. By providing intelligent artificial collaborators that interact with people we amplify human capacity for learning, reasoning, decision making and problem solving. The challenge is to build intelligent systems that augment and amplify rather than replace human intelligence, that leverage our strengths and compensate for our weaknesses. Such Hybrid Intelligence requires meaningful interaction between artificial intelligent agents and humans to negotiate and align goals, intentions and implications of actions. Developing HI needs fundamentally new solutions to core research problems in AI: current AI technology surpasses humans in many pattern recognition and machine learning tasks, but it falls short on general world knowledge, common sense, and the human capabilities of (i) collaboration, (ii) adaptivity, (iii) explanation and (iv) awareness of norms and values. These challenges will be addressed in four interconnected research lines: Collaborative HI: How to design and build intelligent agents that work in synergy with humans, with awareness of each others strengths and limitations? Adaptive HI: HI systems will need to operate in situations not anticipated by their designers, and cope with variable team configurations, preferences and roles. Explainable HI: Intelligent agents and humans need to be able to mutually explain to each other what is happening (shared awareness), what they want to achieve (shared goals), and what collaborative ways they see of achieving their goals (shared plans and strategies Responsible HI: Values such as transparency, accountability, trust, privacy and fairness be an integral part of the design and operation of HI systems. Applications in healthcare, education and science will demonstrate the potential of Hybrid Intelligence: virtual agents and robots will help children with concentration problems to study better; virtual agents and robots will support children in paediatric oncology wards by providing them with entertainment and information during prolonged hospital stays; virtual agents will collaborate with scientists on large scale analysis of the literature, formulate new hypotheses and help design experiments to test them. The team brings together top AI researchers from across the Netherlands in machine learning, knowledge representation, natural language understanding & generation, multi-agent systems, human collaboration, cognitive psychology, multimodal interaction, social robotics, AI & law and ethics of technology. We will initiate a Hybrid Intelligence Centre (HI Centre) to host joint research facilities, multidisciplinary PhD programs, and training and exchange programs. Sustainability of the HI Centre is ensured through tenure track positions with guaranteed long-term funding from the participating universities.

NEPTARGOS project is an interdisciplinary action and joint effort of universities, civil society organizations, research institutions, social scientists, and large companies that seeks to enhance cyber knowledge and understanding of the North Sea maritime infrastructure, fostering security-by-design and resilience-by-design on the modern cyber-physical systems of this maritime environment. In that context, the project will research, develop, validate, and promote an adaptive and holistic Cyber-Physical Security (CPS) governance framework that will foster the design, implementation and enforcement of effective, evidence-based, scenario-centric measures and policies against adversarial cyber-physical attacks.

Electrical power systems occasionally operate close to their stability limits. In future, the increase in generation from remote renewable sources (PV and windgenerators) will intensify stability problems and accordingly, will increase the uncertainty of system reliability. Unexpected disturbances and inadequate system monitoring can cause catastrophic failures, leading to blackouts, as seen worldwide. Existing measurement and control schemes cannot cope with these problems. A common reason for the occurrence of blackouts is lack of coordinated control when the system is affected by large disturbances [1]. In order to prevent large-scale blackouts, extensive research into new applications and radical changes in the complexity of Information and Communication Technology (ICT) is needed. In future, it is expected many fossil-fuel power stations to be replaced by bulk renewables. This raises some questions: What will be the frequency response of the future network after a large disturbance? Will traditional preventive control actions be effective enough? How can they be improved? If not, will new options involving flexible real-time corrective control of conventional and renewable generation provide better solutions? In this project, the main idea is to create a wide-area intelligent system, that empowers future power grids by providing extensive synchronised information in real-time, assessing system vulnerability quickly, and performing timely corrective control actions based on system considerations. The focus of this project will be on the design of a new closed-loop corrective control scheme; elimination of system frequency instability, cascading outages and catastrophic blackouts in existing and future electricity networks.

Children treated for cancer in a hospital often experience high levels of stress and anxiety. Not only are they faced with a life-threatening disease, they also find themselves in a strange environment, surrounded by doctors, nurses and other medical personnel. Moreover, during various types of treatment, close family may not be in close proximity, e.g., due to exposure risks in case of radiation treatment, or simply because parents cannot guarantee 24/7 presence. This project unites and advances the state-of-the-art in automated human-robot interaction design and machine learning to provide children with a humanoid robot companion that is capable of being there when humans cannot and that can address a child’s distress, pain and fear to minimize medical traumatic stress. This is of key importance, e.g., to ensure successful, uneventful treatment, to lower risks associated with involving relatives and clinicians (e.g., radiation exposure).