Abstract
This paper briefly reviews the state-of-the-art in Artificial Intelligence (AI) applied to Air Traffic Management (ATM). The research topics include the application of semantic ontology, multi-agent systems, reinforcement learning (RL), and game theory in ATM. Likewise, this paper also highlights our research advances in this area. In this case, we describe a new Probabilistic Web Ontology Language (PR-OWL) algorithm to enable the reasoning on big datasets in polynomial time. Then, we present the use of both Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms in 4D trajectory management. Next, we describe the usage of Multi-agent Planning (MAP) theory on airport ground handling management. Finally, this paper envisions some research and development directions of AI applied to ATM. It includes: (a) mapping and reducing the gaps between advanced AI technologies and ATM; (b) considering uncertainty in Semantic Ontology for SWIM data exchanging models in ATM; (c) using big data analytics in SWIM; and (d) integrating collaborative ATM technologies towards intelligent SWIM (I-SWIM).
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Weigang, L., Leite, A.F., Ribeiro, V.F., Fregnani, J.A., de Oliveira, I.R. (2017). Towards Intelligent System Wide Information Management for Air Traffic Management. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10656. Springer, Cham. https://doi.org/10.1007/978-3-319-72389-1_46
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