Descrizione del progetto
Intelligenza artificiale e trasparenza nella gestione del traffico aereo
Ci sono grandi aspettative per le tecnologie di intelligenza artificiale (IA) e apprendimento automatico, che favoriranno un importante passo avanti nella gestione del traffico aereo (ATM), consentendo un sistema altamente automatizzato in grado di fornire capacità più elevate. L’affidabilità e la sicurezza di questi sistemi, tuttavia, rimane una questione fondamentale sia per gli utenti che per gli operatori e rappresenta un importante ostacolo per l’adozione di tecnologie di intelligenza artificiale/apprendimento automatico in qualsiasi campo. L’obiettivo principale del progetto TAPAS, finanziato dall’UE, è fornire una serie di principi e criteri che aprano la strada per l’applicazione di queste tecnologie nell’ATM in modo sicuro e affidabile. Le tecniche di intelligenza artificiale spiegabile (XAI), insieme all’analisi visiva, aiuteranno a esplorare i compromessi tra l’efficienza delle implementazioni di IA e l’idoneità per la distribuzione in applicazioni specifiche.
Obiettivo
As Artificial Intelligence (AI) becomes an increasing part of our lives in general, individuals are finding that the need to trust these AI based systems is paramount. Air Traffic Management (ATM) is not an stranger to this: with a system close to, or already at, a saturation level, AI applications are considered a main enabler to reach higher levels of automation.
This would mean a fundamental shift in the automation approach when moving from the classical human-machine interaction to a potentially much richer solution enabled by these AI systems, in which trust in the operations needs to be generated. As humans, operators must be able to fully understand how decisions are being made so that they can trust the decisions of AI systems. The lack of explainability and trust hampers the ability (both individual and global) to fully trust AI systems.
TAPAS aims at exploring highly automated AI-based scenarios through analysis and experimental activities applying eXplainable Artificial Intelligence (XAI) and Visual Analytics, in order to derive general principles of transparency which pave the way for the application of these AI technologies in ATM environments, enabling higher levels of automation.
Specifically, TAPAS will:
• Analyse two operational environments: ATC (Air Traffir Control)Conflict Detection & Resolution (tactical), and Air Traffic Flow Management (pre-tactical). For them, levels of automation 1 to 3 according to SESAR Model will be considered.
• Develop eXplainable Artificial Intelligence (XAI) prototypes addressing the requirements and acceptability criteria of the scenarios.
• Run experiments that assess the applicability of these XAI modules in the higher levels of automation considered, exploring different ways of interaction and information exchange.
• Apply Visual Analytics techniques to contribute to explainability of decissions.
• Extract conclusions, principles and recommendations related to transparency of AI in ATM.
Campo scientifico
Not validated
Not validated
Parole chiave
Programma(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
28022 Madrid
Spagna