Artificial Intelligence and Decision Making
Creators: name: Herbin, Stephane
name: Farges, Jean-Loup
name: Dragos, Valentina
name: Sylvain, Gatepaille
name: Kervarc, Romain
name: Ariane, Piel
name: El Fallah Seghrouchni, Amal
name: Grivault, Ludovic
name: Teichteil-Koenigsbuch, Florent
name: Poveda, Guillaume
name: Pralet, Cedric
name: Roussel, Stephanie
name: Jaubert, Jean
name: Queyrel, Julien
name: Boniol, Frederic
name: Chan-Hon-Tong, Adrien
name: Eudes, Alexandre
name: Le Besnerais, Guy
name: Pagetti, Claire
name: Sanfourche, Martial
name: Nugue, Matthieu
name: Roche, Jean-Michel
name: Trottier, Camille
name: Devillers, Robin
name: Pichillou, Julien
name: Boulch, Alexandre
name: Hurmane, Antoine
name: Colin Koeniguer, Elise
name: Le Saux, Bertrand
name: Trouve, Pauline
name: Caye Daudt, Rodrigo
name: Audebert, Nicolas
name: Brigot, Guillaume
name: Godet, Pierre
name: Le Teurnier, Benjamin
name: Carvalho, Marcela
name: Castillo-Navarro, Javiera
name: Mylvaganam, Thulasi
name: Piet-Lahanier, Helene
Publisher: name: ONERA
Year: 2020
Subjects: AEROSPACE
DEFENSE
ARTIFICIAL INTELLIGENCE
DECISION MAKING
ONTOLOGY
CHRONICLES
PLANNING
MULTI-AGENT SYSTEMS
MACHINE LEARNING
DEEP LEARNING
SEMANTIC MEDIATION
DYNAMIC FUSION
SOFT AND SENSOR DATA
STREAM REASONING
INFORMATION FLOW PROCESSING
KNOWLEDGE REPRESENTATION AND REASONING
COMPLEX EVENT PROCESSING
MULTI-AGENT
MULTI-SENSORS
AIRBORNE PLATFORM
SYSTEMS ARCHITECTURE
SCHEDULING
FORMATION PATH PLANNING
COLLABORATIVE GRAPH SEARCH
FORMATION FLIGHT
COMBINATORIAL OPTIMIZATION
SPACE TELESCOPE
COMPUTER VISION
EMBEDDED SYSTEMS
AERONAUTICAL CERTIFICATION
SCIENTIFIC EXPERIMENTAL DATA
SEMANTIC SEGMENTATION
SHADOWGRAPHY
CRACK DETECTION
EARTH OBSERVATION
SEMANTIC CLASSIFICATION
DETECTION
COREGISTRATION
IMAGE QUALITY ENHANCEMENT
CHANGE DETECTION
3D
WEAK LEARNING
GAME THEORY
ROBUST CONTROL
CONSENSUS

description: Artificial Intelligence (AI) is currently an inescapable keyword in computer science given its predicted huge contribution to the global economy [1] or to the whole society [2, 3], as argued in many recent white papers or reports. Its techniques are expected to provide efficient ways to deal with heterogeneous and voluminous numerical environments and data, help or even improve decision making, and automate complex functions. The aeronautics, spatial and defense domain (ASD) is impacted by this evolution [4]. AI is a research field with a complex history and numerous areas. It is customary to divide these into two trends: Formal and logical, which rely on models, knowledge representation and solvers; Empirical, which rely on data, statistical estimation and inference. Although this last trend mainly drives what is sometimes referred to as the third wave of AI, with Machine Learning playing the key role of a general design principle, it cannot fully solve all problems: data can be rare and costly in the ASD domain, where the requirements of reliability and predictability are often very high. The present issue of the Aerospace Lab journal contains several examples of AI research, from rather specific studies to more general position papers or surveys, which exemplifies these two traditions and, eventually, their possible combination.
description: AerospaceLab Journal, Issue 15, September 2020; ISSN: 2107-6596