This is a graphical model in which a set of random variables possesses the Markov property described by an undirected graph. The Markov network differs from the other graphic model, the Bayesian network, the representation of the dependencies between random variables. It can express some of the dependencies that the Bayesian network cannot express (for example, cyclic dependencies); on the other hand, it cannot express some others.
Published in Chapter:
Artificial Intelligence in Aviation Industries: Methodologies, Education, Applications, and Opportunities
Copyright: © 2020
|Pages: 35
DOI: 10.4018/978-1-7998-1415-3.ch001
Abstract
This chapter presents opportunities to use Artificial Intelligence (AI) in aviation and aerospace industries. The AI used an innovative technology for improving the effectiveness of building aviation systems in each stage of the lifecycle for enhancing the security of aviation systems and the characteristic ability to learn, improve, and predict difficult situations. The AI is presented in Air Navigation Sociotechnical system (ANSTS) because the activity of ANSTS, is accompanied by a high degree of risk of causing catastrophic outcomes. The operator's models of decision making in AI systems are presented such as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, air traffic controllers, engineers, etc. The quality of operator's decisions depends on the development and use of innovative technology of AI and related fields (Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration Decision Making, Blockchain, Artificial Neural Network, etc.).