Human-Autonomous Systems Collective Capability

Project: Research council

Project Details


Human-Autonomous Systems (HAS) are collections of human and autonomous agencies of great importance to, defence, disaster and emergency response, transport, and energy services (especially in hostile/inhospitable environments). However, current reality is that HAS do not provide the right information at the right time to the right agent (human or autonomous); cause information overload; and produce rigid, inflexible and ineffective rule bound behaviours. The current state-of the art in Human-Autonomous Systems is that they often involve disparate, incompatible, and 'stove-piped' communication and information structures with conflicting technologies. This has resulted in failures, ineffectiveness and inefficiency, costing resources and even lives. Improving the collective capability of human-autonomous systems requires agile and flexible behaviour in the face of complex and rapidly changing situations. Developing the collective capability of HAS requires and leads to improving; i) the levels of local and global awareness and utility of information and knowledge, ii) the quality and trustworthiness of decision-making and consideration of alternatives, iii) the ability to increase the level of "command by intent" through the development of lightweight but richer reporting and monitoring mechanisms; and iv) the ability to globally exploit and learn from local initiatives. Underlying all of these lies the importance of the, representation, interactive manipulation and communication of information and knowledge. This 36 month research project will achieve improvements in HAS performance through novel breakthroughs in important areas of Collective Capability for Human-Autonomous Systems (HASCC0. Those breakthroughs will enable improved levels of shared awareness, collective decision-making, agile, responsive command, and collective learning. To achieve this we will develop protocols and technologies for information and knowledge abstraction and representation, argumentation, rationale, command and reporting structures. Our approach is to develop protocols and technologies to support the interactions and knowledge manipulations needed to enhance HAS collective awareness and decision-making and capable of representing and interacting with; - the (rich but lightweight) Argumentation, Rationale, Command and Reporting Structures, - which influence local and global and include strategic, tactical and operational decision-making. Enabling HAS collectives to be agile and responsive. Our investigations comprise two cycles corresponding to different application domain scenarios. Each application domain will present different information and decision-making requirements, and will require different strategic, tactical and operational deployments of HAS. In this way we will seek to assess the generality and wider applicability of our research findings. In the first cycle, we will focus on the situation awareness and decision-making required of HAS for "Multiple Vehicle Cooperative Autonomy". In the second, we will expand our research to investigate HAS for "investigation and repair of defective infrastructure". In each cycle, we will undertake scenario development, modelling, prototyping, evaluation and revision. At the end of each cycle we will produce versions of Protoypes, Models and Principles of HASCC. The research will directly contribute to several EPSRC strategic priority themes by providing science and technology that strengthens critical national infrastructure in: Global Uncertainties - Collective Capability to underpin agile, coherent and integrated HAS, in Defence and Disaster Emergency Response Services Digital Economy - the development of novel Collective Capability Technologies to advance Autonomous Systems, Energy - Collective Capability to underpin HAS enabling safe and reliable energy provision. Transport - Collective Capability for HAS to provide reliable, safe and efficient Transport Services.
Effective start/end date31/03/1230/04/16


  • Engineering and Physical Sciences Research Council


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