Congestion at major airports in the UK and across Europe and the rest of the world is a serious and growing problem. Already Heathrow faces problems occasioned by serious congestion for a major part of the day while at Gatwick demand is expected to exceed capacity for 17 hours per day by 2025. According to a Eurocontrol study, planned capacity at the 138 Eurocontrol Statistical Reference Area (ESRA) airports is expected to increase by 41% in total by 2030, with demand exceeding airport capacity by as much as 2.3 million flights (or 11%) in the most-likely forecast growth scenario. The development and deployment of airport capacity is a major societal issue engendering intense public debate in the UK and around the world. Capacity at congested airports is expressed in slots. A slot identifies a time interval on a specific date during which a carrier is permitted to use the airport infrastructure for landing or take-off. Current slot allocation procedures suffer (inter alia) from the following limitations: 1)Simplistic modelling of the objectives and operational/regulatory constraints bearing on the multiple stakeholders involved in (and affected by) the slot allocation process. 2)Insufficient capture of the interactions encountered in airport networks. 3)The use of empirical or ad hoc processes for determining (rather than computing) declared capacity which address neither the uncertainties involved in airport capacity assessment nor the complexity and size of the real-world problem, even at the single-airport level. Consequently, existing approaches to the allocation of airport capacity fail in a number of critical ways to reflect the complexities presented by the real world. This creates allocation inefficiencies which, in turn, result in poor airport capacity utilisation with significant negative impacts on airport revenues, airline operating costs, the level of service offered to passengers and the environment. There is thus a pressing need to meet the major scientific challenge of developing novel mathematical models and solution approaches to transform the airport slot allocation process and its associated outcomes. The programme grant aims to do just that for a single airport and for a network of airports. Mathematical models will be developed and analysed which consider the objectives and requirements of all stakeholders and which take account of a wide range of operational and regulatory constraints. The intrinsic complexity of the proposed programme and its large scale (especially for the case of the network-wide slot allocation) will mean that it will provide an excellent test-bed for the development of new heuristics and hyper heuristics for large scale complex scheduling problems more widely. Algorithms that will be developed and tested by this project will provide essential support for the complex large scale capacity allocation problems that arise in other types of transportation networks, including rail networks. In addition, it could extend to other types of networks that share similar problem structures, such as those in energy and telecommunications. The models and solution techniques developed will underpin the development of novel decision support systems which have the potential to make a major impact on airport operations. The research team has an internationally leading profile in the areas of mathematical modelling, heuristic development, stochastic optimization, airport slot allocation, airport management and performance assessment. It has an excellent track record of research cooperation with all categories of stakeholders. It will cooperate closely with an impressive array of leading industry stakeholders in order to make sure that the work is as cutting edge industrially as it is scientifically.

Despite being far from having reached 'artificial general intelligence' - the broad and deep capability for a machine to comprehend our surroundings - progress has been made in the last few years towards a more specialised AI: the ability to effectively address well-defined, specific goals in a given environment, which is the kind of task-oriented intelligence that is part of many human jobs. Much of this progress has been enabled by deep reinforcement learning (DRL), one of the most promising and fast-growing areas within machine learning. In DRL, an autonomous decision maker - the "agent" - learns how to make optimal decisions that will eventually lead to reaching a final goal. DRL holds the promise of enabling autonomous systems to learn large repertoires of collaborative and adaptive behavioural skills without human intervention, with application in a range of settings from simple games to industrial process automation to modelling human learning and cognition. Many real-world applications are characterised by the interplay of multiple decision-makers that operate in the same shared-resources environment and need to accomplish goals cooperatively. For instance, some of the most advanced industrial multi-agent systems in the world today are assembly lines and warehouse management systems. Whether the agents are robots, autonomous vehicles or clinical decision-makers, there is a strong desire for and increasing commercial interest in these systems: they are attractive because they can operate on their own in the world, alongside humans, under realistic constraints (e.g. guided by only partial information and with limited communication bandwidth). This research programme will extend the DRL methodology to systems comprising of many interacting agents that must cooperatively achieve a common goal: multi-agent DRL, or MADRL.

The emergence of a global ubiquitous computing environment in which each of us routinely interacts with many thousands of interconnected computers embedded into the everyday world around us will transform the ways in which we work, travel, learn, entertain ourselves and socialise. Ubiquitous computing will be the engine that drives our future digital economy, stimulating new forms of digital business and transforming existing ones.However, ubiquitous computing also carries considerable risks in terms of societal acceptance and a lack of established models of innovation and wealth creation, so that unlocking its potential is far from straightforward. In order to ensure that the UK reaps the benefits of ubiquitous computing while avoiding its risks, we must address three fundamental challenges. First, we need to pursue a new technical research agenda for the widespread adoption of ubiquitous computing. Second, we must understand and design for an increasingly diverse population of users. Third, we need to establish new paths to innovation in digital business. Meeting these challenges requires a new generation of researchers with interdisciplinary skills in the technical and human centred aspects of ubiquitous computing and transferable skills in research, innovation and societal impact.Our doctoral training centre for Ubiquitous Computing in the Digital Economy will develop a cohort of interdisciplinary researchers who have been exposed to new research methods and paradigms within a creative and adventurous culture so as to provide the future leadership in research and knowledge transfer that is necessary to secure the transformative potential of ubiquitous computing for the UK digital economy. To achieve this we will work across traditional research boundaries; encourage students to adopt an end-to-end perspective on innovation; promote creativity and adventure in research; and place engagement with society, industry and key stakeholders at the core of our programme.Our proposal brings together a unique pool of researchers with extensive expertise in the technologies of ubiquitous and location based computing, user-centred design, societal understanding, and research and training in innovation and leadership. It also involves a wide spectrum of industry partners from across the value chain for ubiquitous computing, spanning positioning, communications, devices, middleware, databases, design, and our two driving market sectors of the creative industries and transportation.Our training programme is based on the approach of personalised pathways that develop individual students' interdisciplinary and transferable skills, and that produce a personal portfolio to showcase the skills and experience gained alongside the more traditional PhD thesis. It includes a flexible taught programme that emphasises student-led seminars, short-fat modules, training projects and e-learning as delivery mechanisms that are suited to PhD training; an industrial internship scheme under which students spend three months working at an industrial partner; and a PhD research project that builds on a proposal developed during the first year of training and that is supported by multiple supervisors from different disciplines with industry involvement. Our DTC will foster a community of researchers through a dedicated shared space, a programme of community building events, training for supervisors and well as students, funding for a student society, and an alumni programme.