Jiangjun Tang

Augmented Cognition using Real-time EEG-based Adaptive Strategies for Air Traffic Control

Future air traffic systems aim at increasing both the capacity and safety of the system, necessitating the development of new metrics and advisory tools for controllers’ workload in real-time. Psychophysiologi-cal data such as Electroencephalography (EEG) are used to contrast and validate subjective assessments and workload indices. EEG used within augmented cognition systems form situation awareness advisory tools that are able to provide real-time feedback to air-traffic control supervisors and planners. This aug-mented cognition system and experiments using the system with air traffic controllers are presented. Traf-fic indicators are used in conjunction with EEG-driven cognitive indicators to adapt the traffic in real-time through Computational Red Teaming (CRT) based adaptive control mechanisms. The metrics, measures, and adaptive control mechanisms are described and evaluated. The best mechanism to improve system ef-ficacy was found when the system allowed for real-time adaptation of traffic based on engagement met-rics driven from the EEG data.

A multi-objective evolutionary method for Dynamic Airspace Re-sectorization using sectors clipping and similarities

Dynamic Airspace Sectorization (DAS) is a future concept in Air Traffic Management. Its main goal is to increase airspace capacity by reshaping - thus optimizing - airspace sector boundaries based on the specifics of different air traffic situations, weather conditions and other factors. The primary objective for the optimization is to balance and reduce the workload of Air Traffic Controllers (ATCs). Many researchers have made efforts in this topic in the past years. However, air traffic changes continually, and DAS has to be adaptive to each change; be it in terms of aircraft density, dynamic routes, fleet mix, etc. Therefore, instead of sectorizing the airspace each time a change occurs, we should re-sectorize it by maintaining maximum similarities between each sectorization. In this paper, we propose a multi-objective evolutionary computation methodology to re-sectorize an airspace. We use a similarity measure between the existing sectorization and the re-sectorization as an objective to maximize during the evolution.We test the methodology with different air traffic conditions with four objective functions: minimize ATC task load standard deviation, maximize average flight sector time, maximize the minimum distance between traffic crossing points and sector boundaries, and maximize the similarity of two airspace sectorizations. Experimental results show that our re-sectorization method is able to perform airspace re-sectorization under different changes in the air traffic, while satisfying the predefined objectives.

Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms

Usually, Evolutionary Computation (EC) is used for optimisation and machine learning tasks. Recently, a novel use of EC has been proposed – Multiobjective Evolutionary Based Risk Assessment (MEBRA). MEBRA characterises the problem space associated with good and inferior performance of computational algorithms. Problem instances are represented (“scenario Representation”) and evolved (“scenario Generation”) in order to evaluate algorithms (“scenario Evaluation”). The objective functions aim at maximising or minimising the success rate of an algorithm. In the “scenario Mining” step, MEBRA identifies the patterns common in problem instances when an algorithm performs best in order to understand when to use it, and in instances when it performs worst in order to understand when not to use it.

On the Role of Working Memory in Trading-Off Skills and Situation Awareness in Sudoku

Working memory accounts for the ability of humans to perform cognitive processing, by handling both the representation of information (the mental picture forming the situation awareness) and the space required for processing these information (skill processing). The more complex the skills are, the more processing space they require, the less space becomes available for storage of information. This interplay between situation awareness and skills is critical in many applications. Theoretically, it is less understood in cognition and neuroscience. In the meantime, and practically, it is vital when analysing the mental processes involved in safety-critical domains. In this paper, we use the Sudoku game as a vehicle to study this trade-off. This game combines two features that are present during a user interaction with a software in many safety critical domains: scanning for information and processing of information. We use a society of agents for investigating how this trade-off influences players proficiency.

An evolutionary goal-programming approach towards scenario design for air-traffic human-performance experiments

Air traffic controllers are responsible for maintaining a safe and efficient flow of air traffic in controlled airspace. Many aspects of air traffic control are impacted by the performance of air traffic controllers such as separation assurance tasks. In order to design more advanced air traffic management systems, there is a need for more experiments to be conducted which evaluate the impact of human performance on the system. The design of scenarios that satisfy/meet specific traffic characteristics needed by the analyst is a daunting task. For example, it is often required to design scenarios for a specific sector that have a specific number of conflicts to evaluate human task load. To schedule the aircraft within the time specified for the experiment, and given all the constraints imposed by the route structure and airspace design parameters for the sector in question, are far from trivial problems. In this paper, an evolutionary goal programming approach has been presented which generates a set of scenarios for use in these experiments. The evolutionary goal programming system aimed to generate scenarios meeting the criteria of a set number of conflicts in each of four conflict angle groups. Differential evolution was employed in addition to three modified methods for the optimization of the problem. It was found that the three modified methods outperformed the standard method by producing a greater number of scenarios meeting the set criteria.

Modelling and evolutionary multi-objective evaluation of interdependencies and work processes in airport operations

An airport is a multi-stakeholders environment, with work processes and operations cutting across a number of organizations. Airport landside operations involve a variety of services and entities that interact and depend on each others. In this paper, we introduce the Landside Modelling and Analysis of Services (LAMAS) tool, which is a multi-agent system, to simulate, analyze and evaluate the interdependencies of services in airport operations. A genetic algorithm is used to distribute resources among the different entities in an airport such that the level of service is maintained. The problem is modelled as a multi-objective constrained resource allocation problem with the objective functions being the maximization of quality of service while reducing the total cost.

An interactive evolutionary computation framework controlled via EEG signals

This paper presents an EEG-based interactive genetic algorithm framework, with the goal of leveraging EEG signals collected from a human expert involved in the evaluation of interactive genetic algorithm as inputs for genetic parameter control. We explain the framework of the system and our cognitive model constructed based on a 19 channel EEG system. An experiment has been performed to test the effectiveness of our framework and our cognitive model. Our work is the first attempt to combine brain-computer interaction with interactive evolutionary computation and parameter control.

Behavioral learning of aircraft landing sequencing using a society of Probabilistic Finite state Machines

Air Traffic Control (ATC) is a complex safety critical environment. A tower controller would be making many decisions in real-time to sequence aircraft. While some optimization tools exist to help the controller in some airports, even in these situations, the real sequence of the aircraft adopted by the controller is significantly different from the one proposed by the optimization algorithm. This is due to the very dynamic nature of the environment. The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing. This aim is tested in this paper by attempting to learn sequences generated from a well-known sequencing method that is being used in the real world. The approach relies on a genetic algorithm (GA) to learn these sequences using a society Probabilistic Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus, decomposing the learning problem into a group of agents that need to work together to learn the overall problem. Three sequence metrics (Levenshtein, Hamming and Position distances) are compared as the fitness functions in GA. As the results suggest, it is possible to learn the behavior of the algorithm/heuristic that generated the original sequence from very limited information.

Shaping Influence and Influencing Shaping: A Computational Red Teaming Trust-Based Swarm Intelligence Model

Sociotechnical systems are complex systems, where nonlinear interaction among different players can obscure causal relationships. The absence of mechanisms to help us understand how to create a change in the system makes it hard to manage these systems.

Bio-inspired robotics for air traffic weather information management

With the increase in automation to serve the growing needs and challenges of aviation, air traffic controllers (ATCs) are now faced with an information overload from a myriad of sources, both in graphical and textual format. One such source is weather information, which is typically comprised of wind speed, wind direction, thunderstorms, cloud cover, icing, temperature and pressure at various altitudes. This information requires domain expertise to interpret and communicate to ATCs, who then employ this information to manage air traffic efficiently and safely. Unfortunately, ATCs are not trained meteorologists, so there are significant challenges associated with the correct interpretation and utilization of this information by ATCs. In this paper, we propose a bio-inspired weather robot, which interacts with the air traffic environment and provides targeted weather-related information to ATCs by identifying which airspace sectors they are working on. It uses bio-inspired techniques for processing weather information and path planning in the air traffic environment and is fully autonomous in the sense that it only interacts with the air traffic environment passively and has an onboard weather information processing system. The weather robot system was evaluated in an experimental environment with live Australian air traffic, where it successfully navigated the environment, processed weather information, identified airspace sectors and delivered weather-related information for the relevant sector using a synthetic voice.