Deliverables Documents, reports (12) Final Project Results Report Report including the final publishable summary report and all the technical activities performed during the whole project It includes an assessment of the project achievements towards the RD initial goals An initial version will be available at M22 TAPAS Validation Plan This document develops a clear plan of the validation activities that will be conducted within the project for each one of the target domains CDR and ATFM including the scope of the validation approach and methodology relevant criteria to be assessed and humanspecific measures that need to be gathered for the defined scenarios Consolidated Requirements and Functional Roadmap This document will deliver a merge of contextual requirements from both the operational and technical use cases descriptions, as well as a roadmap of delegation of functionalities between human actors and the machine. Use Cases Transparency Requirements This document will consolidate the transparency requirements derived from the human machine interactions for each level of automation in the ATFM and CDR operational use cases, and that will need to be accomplished during the implementation activities. A first draft will be available at M6 for the ATFM transparency requirements. TAPAS Validation Report This document will consolidate the results of the analysis performed based on the feedback and data gathered during the execution of the ATFM and CDR experiments including objectives achieved further research and recommendations for derivation of transparency criteria A first draft will be available at M14 containing the preliminary results of the ATFM experiment TAPAS Integrated Prototype Document that contains the description of the ATFM and CDR prototype including functionalities and improvements derived from the validation activities conducted A first draft will be available at M11 describing the ATFM prototype Principles for Transparency in AI/ML automation in ATM This document constitutes the final identification of the requirements that will ensure that different transparencyexplainability criteria are fulfilled when AIMLbased models are implemented This document will take as input the results from the validation activities conducted along the project and will combine analysis and expertise to specify the requirements A first draft will be available at M14 Reference of XAI Methods This document will provide an identification of promising state of the art explainable reinforcement learning methods and deliver a reference of the XAI techniques for detecting problems and prescribing solutions in the ATFM and CDR operational cases A first draft will be available at M12 delivering a reference for the ATFM prototype Visualizations and Visual Analytics methods Description of the Visualizations and Visual Analytics methods implemented to improve the explainability and understandability of XAI ATFM and CDR Operational Cases A first draft will be available at M12 containing the description of the visualizations and visual analytics methods implemented for the ATFM use case Exploitation and Dissemination Plan This deliverable comprises the planning of the dissemination exploitation and communication activities TAPAS Use Cases Description This document will describe in detail the use cases to be developed under TAPAS project: (a) from the operational point of view; and (b) from the technological perspective, providing also additional requirements and feasibility analysis of the solutions proposed. Exploitation and Dissemination Report This deliverable comprises the results of the implementation of the dissemination exploitation and communication activities according to the Exploitation and Dissemination Plan A first draft will be available at M24 Publications Conference proceedings (1) Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers Author(s): George Vouros, George Papadopoulos, Alevizos Bastas, Jose Manuel Cordero, Ruben Rodriguez Rodriguez Published in: Volume 351: PAIS 2022, 2022 Publisher: PAIS (Prestigious Applications of Intelligent Systems) DOI: 10.48550/arxiv.2206.07403 Peer reviewed articles (4) Supporting Visual Exploration of Iterative Job Scheduling Author(s): Gennady Andrienko, Natalia Andrienko, Jose Manuel Cordero Garcia, Dirk Hecker, George A. Vouros Published in: IEEE Computer Graphics and Applications, Issue 02721716, 2022, ISSN 0272-1716 Publisher: Institute of Electrical and Electronics Engineers DOI: 10.1109/mcg.2022.3163437 Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management Author(s): Theocharis Kravaris, Konstantinos Lentzos, Georgios Santipantakis, George A. Vouros, Gennady Andrienko, Natalia Andrienko, Ian Crook, Jose Manuel Cordero Garcia & Enrique Iglesias Martinez Published in: Applied Intelligence, Issue 0924669X, 2022, ISSN 0924-669X Publisher: Kluwer Academic Publishers DOI: 10.1007/s10489-022-03605-1 Visual Analytics for Human-Centered Machine Learning Author(s): N. Andrienko, G. Andrienko, L. Adilova and S. Wrobel Published in: IEEE Computer Graphics and Applications, Issue 02721716, 2022, ISSN 0272-1716 Publisher: Institute of Electrical and Electronics Engineers DOI: 10.1109/mcg.2021.3130314 Explainable Deep Reinforcement Learning: State of the Art and Challenges Author(s): George A. Vouros Published in: ACM Computing Surveys, Issue 03600300, 2022, ISSN 0360-0300 Publisher: Association for Computing Machinary, Inc. DOI: 10.1145/3527448 Book chapters (1) Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge Author(s): Theocharis Kravaris and George A. Vouros Published in: Multi-Agent Technologies and Machine Learning, 2022 Publisher: IntechOpen DOI: 10.5772/intechopen.105627 Searching for OpenAIRE data... There was an error trying to search data from OpenAIRE No results available
