Descrizione del progetto
Algoritmi migliorati per la sicurezza e l’affidabilità nella gestione del traffico aereo
Tra gli altri campi in cui vengono applicate, le soluzioni di intelligenza artificiale (IA) sono ampiamente utilizzate per sostenere le attività del processo decisionale in materia di gestione del traffico aereo. Ciononostante, l’affidabilità di tali soluzioni è messa in dubbio, in quanto le decisioni fornite non sono sempre chiare o comprensibili per gli operatori umani. Il progetto ARTIMATION, finanziato dall’UE, introdurrà metodi di IA innovativi volti a prevedere il traffico nel trasporto aereo e a ottimizzare i relativi flussi sulla base della sfera dell’intelligenza artificiale spiegabile. ARTIMATION intende garantire un supporto decisionale sicuro e affidabile, dando priorità a modelli di IA trasparenti che offrano garanzie di visualizzazione, spiegazione, generalizzazione e adattabilità nel corso del tempo.
Obiettivo
Recently, Artificial intelligence (AI) algorithms have shown increasable interest in various application domains including in Air Transportation Management (ATM). Different AI in particular Machine Learning (ML) algorithms are used to provide decision support in autonomous decision-making tasks in the ATM domain e.g. predicting air transportation traffic and optimizing traffic flows. However, most of the time these automated systems are not accepted or trusted by the intended users as the decisions provided by AI are often opaque, non-intuitive and not understandable by human operators. Safety is the major pillar to air traffic management, and no black box process can be inserted in a decision-making process when human life is involved. In order to address this challenge related to transparency of the automated system in the ATM domain, ARTIMATION focuses on investigating AI methods in predicting air transportation traffic and optimizing traffic flows based on the domain of Explainable Artificial Intelligence (XAI). Here, AI models’ explainability in terms of understanding a decision i.e. post hoc interpretability and understanding how the model works i.e. transparency can be provided in the air traffic management. In predicting air transportation traffic and optimizing traffic flows systems, ARTIMATION will provide a proof-of-concept of transparent AI models that includes visualization, explanation, generalization with adaptability over time to ensure safe and reliable decision support.
Campo scientifico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systems
- social sciencessociologyindustrial relationsautomation
- social sciencessocial geographytransporttransport planningair traffic management
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
722 20 VASTERAAS
Svezia