Descripción del proyecto
Algoritmos mejorados para la seguridad y fiabilidad de la gestión del tráfico aéreo
Entre otros ámbitos, las soluciones de inteligencia artificial (IA) se utilizan ampliamente para respaldar tareas de toma de decisiones en la gestión del transporte aéreo. Sin embargo, su fiabilidad se encuentra cuestionada porque las decisiones proporcionadas no son siempre claras o no son comprensibles para los operadores humanos. El proyecto ARTIMATION, financiado con fondos europeos, introducirá innovadores métodos de IA para predecir el tráfico del transporte aéreo y para optimizar los flujos del tráfico basados en el dominio de la inteligencia artificial explicable. El objetivo de ARTIMATION es garantizar la ayuda segura y fiable en las decisiones, al centrarse en modelos de IA transparentes que incluyan visualización, explicación y generalización con adaptabilidad con el tiempo.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
Régimen de financiación
RIA - Research and Innovation actionCoordinador
722 20 VASTERAAS
Suecia