Nowadays the evolution of Human Factor's models has included implementation of Artificial Intelligence (AI) in aviation as an innovative technology for enhancing security and the characteristic ability to learn, improve, and predict. The AI is presented in models of decision making in Air Navigation Sociotechnical system as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, for air traffic controllers in the emergencies.
Nowadays in documents of International Civil Aviation Organization (ICAO) defined
new added approaches for achieving the main goal of ICAO enhancing the
effectiveness of global aviation security, and improving the practical and sustainable
implementation of preventive aviation security measure. The Global Aviation Security Plan
(GASP) identifies five key outcomes for improving effectiveness, such as [
So, the quality of decisions dependences from the development and using of
innovative technology in aviation nowadays such as Artificial Intelligence (AI) [
The purposes of the work are: analysis of the benefits of using AI models in the Air Navigation Sociotechnical System (ANSTS); AI models and methods; problems of CDM by ANS personnel (human-operators (H-O): pilots of manned and unmanned aerial vehicles (UAV), air traffic controllers (ATC), engineers). 2
Today, AI capabilities are proliferating across the transport sector. The AI systems
have high potential in Air Traffic Management (ATM), specifically in areas which
involve decision making (DM) under uncertainty (e.g. conflict detection and
resolution) and prediction with limited information (e.g. trajectory prediction) [3; 4]. For
example, validation based on one month of ADS-B data, the AI system is able to
predict ATC actions, for complex traffic scenarios, at an accuracy –
AN-Conf/13WP/232 of above 70%. In addition to developing solutions to support en-route
operations, AI can be applied in speech recognition to act as an additional safety net to
detect read-back errors; trajectory synchronization of aircraft ground movements that
provide optimised taxiing strategies that comprehensively accounts for arrivals and
departures as well; and predicting the most optimal runway configuration for a given
arrival sequence and departure schedule so as to maximize the runway throughput [
Therefore, it is necessary to present ANS as STS and to applicate AI methods for development capacity of ANSTS. Examples of applied AI in education course “Informatics of Decision Making” for aviation students and future personal of ANS in Table 1. AI is the simulation of human intelligence processes by modeling, computer systems, and machines. These processes include: learning (the acquisition of information and rules for using the information); reasoning, estimation, and modeling (using rules to reach conclusions (approximate or definite results)); self-correction (estimation of obtained models); particular applications of AI include ES; DSS; automated systems; systems of pattern recognition, speech recognition, and machine vision, etc. Many cases of using AI technology are driven by emergence, availability, and accessibility. The differentiating factor of an AI system from a standard software system is the characteristic ability to learn, improve, and predict. Through training, an AI system is able to generate knowledge and apply it to novel situations not encountered before. In AI, an ES is a computer system that simulates the decision making ability of a human. The ESs are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. The first ESs were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of AI software. The ICAO documents recommend developing Intelligent Expert Systems in aviation to support DM of operators [1; 2]. For the education of aviation personals in AI developed training, such as: Expert Judgment Method (EJM) / Multi-criteria decision problems. Deterministic models. Network planning. Decision making in an emergency. Stochastic models. Decision making in Risk. Decision making in an emergency.
Game Theory. DM in uncertainty. Optimal aerodrome of landing. Dynamic programming (DP) and GRID analyzes of the problem. The DP method to solve the problem of minimal cost, climbing an aircraft. For example, training 1 “Expert Judgment Method (EJM) / Multi-criteria decision problems” [5; 6]:
Theory. Basic of EJM for ANS (Classification of methods of DM. The algorithm of EJM. The matrix of individual and group preferences. Coordination of experts’ opinion. Multi-criteria decision problems).
Practice. Tasks: Quantitative estimation of the complexity of the aircraft flight. Definition of significance (complexity) of the phases of flight of the aircraft (Fig. 1).
To show the DSSs we use tasks of alternate aerodrome/place in the case of an
emergency landing (difficult meteorological conditions, etc.) by the method of DM in
uncertainty is means of the criteria of DM in uncertainty: Wald, Laplace, Savage,
Hurwicz [
The problem of optimizing the interaction between pilot and ATC can be solved by the way of development and synchronization (maximal alignment over time) of deterministic models of H-O CDM, which will minimize the critical time needed to solve EC, by definition the optimal sequence of execution of technological procedures. The parallel process of simultaneous execution of pilot and ATC technological operations in the emergency case can be represented as a consolidated dual-channel network. For a consistent optimization of such a network in order to achieve the cross-cutting efficacy of joint decisions, it is advisable to use a multi-criteria approach: achieving a minimum time for parity of emergency case with maximum safety / maximum harmonization over the time of H-O actions.
In this context, the use of flight simulators during ATC professional training is relevant. They will help ATC’s to get acquainted with the situation in the flight crew cabin and the parameters of the aircraft's devices during the emergency case. At the same time the ATC: will receive the experience of the crew members during the emergency case; will pay attention to how the intervention of the dispatcher can disrupt crew members; will complete exercises on the use of radio during the emergency case; will complete the checklist in the emergency case; will participate in captain decision making during the emergency case; will observe the features of the goaround procedure. In the emergency case, ATC is advised to use a checklist that will help to handle incidents in order to establish optimal actions to achieve better cooperation between pilot and ATC. A supervisor who works with ATC, using a checklist, can provide better support as it will more clearly understand the traffic control in emergencies.
The AI technologies in aviation were clustered in the following seven capabilities: Machine Learning (ML); Natural Language Processing (NLP); Expert Systems; Vision and Speech; Planning; Robotics.
Further research should be directed to the solution of the problem in prerequisites of emergency situations and preventing catastrophic situations too. Models of flight emergency development and of DM by an operator in-flight emergency will allow predicting the operator’s actions with the aid of the informational-analytic and diagnostics complex for research of operator behavior in extreme situations. It is necessary to develop modern DSSs of Air Navigation System’s operator (pilots, air traffic controllers, flight dispatchers, UAV’s operators) in-flight emergencies and in other situations, to investigate applied tasks of the DM in Sociotechnical System by an operator of aviation system, chemical production, energy, military industry, etc.
Developing of Intelligent ES, DSSs considering new concepts in aviation (FF-ICE, PBA, SMART, CDM, SWIM, etc.) for different operators and each stage, process, which are problems, with using modern information technologies Data Science, Big Data, Data Mining, Multi-Criteria Decision Analysis, etc. It is necessary to analyze all factors influencing the DM of operators in these systems in order to predict the development of the technogenic catastrophe and prevent it with AI.