Ports are a great example of heterogeneous information hubs. Multiple stakeholders operate inside and outside them with different motivations and businesses. Although document and data interchange is already in place through Port Community Systems (PCS), the interchange is limited to official documentation and services of the Port Authority, such as custom declarations, import/export of cargo, and other formal documents. However, an effective integration of operational data is far from optimal in most ports, and especially so in medium or small ports, where budget is limited and IT services usually is outsourced. In contrast, the available operational data (resources tracking, container status, vessel operations, surface or berth available, air/water quality measurements,...) is constantly increasing and technology is getting inexpensive and widely available. However, the application of such systems is still single-entity centric, since the information is not shared, keeping the real potential of the Internet of Things (IoT) and Industry 4.0 hidden. The same holds for geographic areas surrounding ports, where Smart Cities integrate various data systems and provide valuable services to citizens and authorities. PIXEL will enable a two-way collaboration of ports, multimodal transport agents and cities for optimal use of internal and external resources, sustainable economic growth and environmental impact mitigation, towards the Ports of the Future. PIXEL will leverage technological enablers to voluntary exchange data among ports and stakeholders, thus ensuring a measurable benefit in this process. The main outcome of this technology will be efficient use of resources in ports, sustainable development and green growth of ports and surrounding cities/regions. Built on top of the state-of-the art interoperability technologies, PIXEL will centralise data from the different information silos where internal and external stakeholders store their operational information.

A significant, highly complex class of artificial intelligence applications are sequential decision-making problems, where a sequence of actions needs to be planned and taken to achieve a desired goal. Examples include routing problems, which involve a sequence of steps from source to destination; the control of manufacturing processes, which consist of a variable sequence of operations; or active learning problems, in which machine learning algorithms query human users for a sequence of inputs. We address the compelling scientific and technological goal of tackling users' lack of trust in AI, which currently often hinders the acceptance of AI systems. We break down this problem into two complementary aspects. First, users do not understand current AI systems well, with a lack of transparency leading to misinterpretations and mistrust. Second, current AI systems do not understand users well, offering solutions that are inadequately tailored to the users' needs and preferences. PEER will focus on how to systematically put the user at the centre of the entire AI design, development, deployment, and evaluation pipeline, allowing for truly mixed human-AI initiatives on complex sequential decision-making problems. The central idea is to enable a two-way communication flow with enhanced feedback loops between users and AI, leading to improved human-AI collaboration, mutual learning and reasoning, and thus increased user trust and acceptance. As an interdisciplinary project between social sciences and artificial intelligence, PEER will facilitate novel ways of engagement by end-users with AI in the design phase; will create novel AI planning methods for sequential settings which support bidirectional conversation and collaboration between users and AI; will develop an AI acceptance index for the evaluation of AI systems from a human-centric perspective; and will conduct an integration and evaluation of these novel approaches in several real-world use cases.