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  1. Article ; Online: Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management.

    Kravaris, Theocharis / Lentzos, Konstantinos / Santipantakis, Georgios / Vouros, George A / Andrienko, Gennady / Andrienko, Natalia / Crook, Ian / Garcia, Jose Manuel Cordero / Martinez, Enrique Iglesias

    Applied intelligence (Dordrecht, Netherlands)

    2022  Volume 53, Issue 4, Page(s) 4063–4098

    Abstract: With the objective to enhance human performance and maximize engagement during the performance of tasks, we aim to advance automation for decision making in complex and large-scale multi-agent settings. Towards these goals, this paper presents a deep ... ...

    Abstract With the objective to enhance human performance and maximize engagement during the performance of tasks, we aim to advance automation for decision making in complex and large-scale multi-agent settings. Towards these goals, this paper presents a deep multi agent reinforcement learning method for resolving demand - capacity imbalances in real-world Air Traffic Management settings with thousands of agents. Agents comprising the system are able to jointly decide on the measures to be applied to resolve imbalances, while they provide explanations on their decisions: This information is rendered and explored via appropriate visual analytics tools. The paper presents how major challenges of scalability and complexity are addressed, and provides results from evaluation tests that show the abilities of models to provide high-quality solutions and high-fidelity explanations.
    Language English
    Publishing date 2022-06-06
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1479519-X
    ISSN 1573-7497 ; 0924-669X
    ISSN (online) 1573-7497
    ISSN 0924-669X
    DOI 10.1007/s10489-022-03605-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning

    Bastas, Alevizos / Kravaris, Theocharis / Vouros, George A.

    2020  

    Abstract: The current Air Traffic Management (ATM) system worldwide has reached its limits in terms of predictability, efficiency and cost effectiveness. Different initiatives worldwide propose trajectory-oriented transformations that require high fidelity ... ...

    Abstract The current Air Traffic Management (ATM) system worldwide has reached its limits in terms of predictability, efficiency and cost effectiveness. Different initiatives worldwide propose trajectory-oriented transformations that require high fidelity aircraft trajectory planning and prediction capabilities, supporting the trajectory life cycle at all stages efficiently. Recently proposed data-driven trajectory prediction approaches provide promising results. In this paper we approach the data-driven trajectory prediction problem as an imitation learning task, where we aim to imitate experts "shaping" the trajectory. Towards this goal we present a comprehensive framework comprising the Generative Adversarial Imitation Learning state of the art method, in a pipeline with trajectory clustering and classification methods. This approach, compared to other approaches, can provide accurate predictions for the whole trajectory (i.e. with a prediction horizon until reaching the destination) both at the pre-tactical (i.e. starting at the departure airport at a specific time instant) and at the tactical (i.e. from any state while flying) stages, compared to state of the art approaches.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-05-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Explaining deep reinforcement learning decisions in complex multiagent settings

    Kravaris, Theocharis / Lentzos, Konstantinos / Santipantakis, Georgios / Vouros, George A. / Andrienko, Gennady / Andrienko, Natalia / Crook, Ian / Garcia, Jose Manuel Cordero / Martinez, Enrique Iglesias

    towards enabling automation in air traffic flow management

    2022  

    Abstract: With the objective to enhance human performance and maximize engagement during the performance of tasks, we aim to advance automation for decision making in complex and large-scale multi-agent settings. Towards these goals, this paper presents a deep ... ...

    Abstract With the objective to enhance human performance and maximize engagement during the performance of tasks, we aim to advance automation for decision making in complex and large-scale multi-agent settings. Towards these goals, this paper presents a deep multi agent reinforcement learning method for resolving demand - capacity imbalances in real-world Air Traffic Management settings with thousands of agents. Agents comprising the system are able to jointly decide on the measures to be applied to resolve imbalances, while they provide explanations on their decisions: This information is rendered and explored via appropriate visual analytics tools. The paper presents how major challenges of scalability and complexity are addressed, and provides results from evaluation tests that show the abilities of models to provide high-quality solutions and high-fidelity explanations.

    Online First
    Keywords Air traffic management ; Explainability ; Interpretability ; Multi-agent deep reinforcement learning ; Stochastic decision trees ; Visualization
    Subject code 629 ; 006
    Language English
    Publishing date 2022-06-06
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods

    Kravaris, Theocharis / Spatharis, Christos / Bastas, Alevizos / Vouros, George A. / Blekas, Konstantinos / Andrienko, Gennady / Andrienko, Natalia / Garcia, Jose Manuel Cordero

    2019  

    Abstract: In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic Management (ATM) domain. Specifically, we aim to ... ...

    Abstract In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic Management (ATM) domain. Specifically, we aim to resolve cases where demand of airspace use exceeds capacity (demand-capacity problems), via imposing ground delays to flights at the pre-tactical stage of operations (i.e. few days to few hours before operation). Casting this into the multiagent domain, agents, representing flights, need to decide on own delays w.r.t. own preferences, having no information about others' payoffs, preferences and constraints, while they plan to execute their trajectories jointly with others, adhering to operational constraints. Specifically, we formalize the problem as a multiagent Markov Decision Process (MA-MDP) and we show that it can be considered as a Markov game in which interacting agents need to reach an equilibrium: What makes the problem more interesting is the dynamic setting in which agents operate, which is also due to the unforeseen, emergent effects of their decisions in the whole system. We propose collaborative multiagent reinforcement learning methods to resolve demand-capacity imbalances: Extensive experimental study on real-world cases, shows the potential of the proposed approaches in resolving problems, while advanced visualizations provide detailed views towards understanding the quality of solutions provided.
    Keywords Computer Science - Multiagent Systems ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2019-12-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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