Analysis and design of multiagent systems using MAS-CommonKADS

This article proposes an agent-oriented methodology called MAS-CommonKADS and develops a case study. This methodology extends the knowledge engineering methodology CommonKADSwith techniquesfrom objectoriented and protocol engineering methodologies. The methodology consists of the development of seven models: Agent Model, that describes the characteristics of each agent; Task Model, that describes the tasks that the agents carry out; Expertise Model, that describes the knowledge needed by the agents to achieve their goals; Organisation Model, that describes the structural relationships between agents (software agents and/or human agents); Coordination Model, that describes the dynamic relationships between software agents; Communication Model, that describes the dynamic relationships between human agents and their respective personal assistant software agents; and Design Model, that refines the previous models and determines the most suitable agent architecture for each agent, and the requirements of the agent network

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

Status of NASA/Army rotorcraft research and development piloted flight simulation

The status of the major NASA/Army capabilities in piloted rotorcraft flight simulation is reviewed. The requirements for research and development piloted simulation are addressed as well as the capabilities and technologies that are currently available or are being developed by NASA and the Army at Ames. The application of revolutionary advances (in visual scene, electronic cockpits, motion, and modelling of interactive mission environments and/or vehicle systems) to the NASA/Army facilities are also addressed. Particular attention is devoted to the major advances made in integrating these individual capabilities into fully integrated simulation environment that were or are being applied to new rotorcraft mission requirements. The specific simulators discussed are the Vertical Motion Simulator and the Crew Station Research and Development Facility

Dispatcher3 – Machine learning for efficient flight planning - Approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decision making processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general

Dispatcher3 – Machine learning for efficient flight planning: approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decisionmaking processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general.This work is performed as part of Dispatcher3 innovation action which has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreements No 886461. The Topic Manager is Thales AVS France SAS. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union. The opinions expressed herein reflect the authors’ views only. Under no circumstances shall the Clean Sky 2 Joint Undertaking be responsible for any use that may be made of the information contained herein.Postprint (published version

A parallel implementation of a multisensor feature-based range-estimation method

There are many proposed vision based methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. All methods, however, will require very high processing rates to achieve real time performance. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates varying from ten frames per second to thirty or more frames per second depending on the vehicle speed. Such a system will need to sustain billions of operations per second. To reach such high processing rates using current technology, a parallel implementation of the obstacle detection/ranging method is required. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for helicopter flight, realized on both a distributed-memory and shared-memory parallel computer