Monthly Archives: February 2014


Since the Wright brothers first took flight, the progress of mankind in the domain of flight has been nothing short of spectacular. The progress of technology, faster aircraft, in­strument flight, and increased air traffic resulted in establish­ing a governmental infrastructure to control air traffic. Dur­ing the late 1920s air traffic control was established as a profession. Rules, procedures, standards, and facilities were designed to provide for safe and orderly traffic patterns and required separation between the aircraft (1). Stakeholders of the airspace system include airline operators, airport authori­ties, weather specialists, and air traffic controllers (ground, tower, en-route, flow), as well as the passengers. In addition to civilian users, including the airlines and general aviation, the system must accommodate military and space flight activ­ities.

The demand for air transport has steadily increased since introduction of jet aircraft during the late 1950s. The safety and effectiveness of the national airspace system (NAS) de­pends on the performance of the air traffic management (ATM) personnel—the employees of the Federal Aviation Ad­ministration (FAA). The increasing complexity of the system and proliferation of computing equipment have generated an urgent need to explore the possibility of supporting the hu­man component of the system with tools and techniques based on the concepts and methods of artificial intelligence (AI) (2).

Intelligence is defined as ‘‘the ability to learn or under­stand from experience, ability to acquire and retain knowl­edge’’ (3). Applied AI is about programming computers to per­form tasks previously assumed to require human intelligence. The usefulness of AI is the measure of its success. The key issue is realization that knowledge must be represented ex­plicitly in terms of its nonalgorithmic contents. The computer program acts upon it by deduction and reasoning applying various search algorithms. There is a need to create software products representing an artificial expertise—a container for limited-domain knowledge. This development is particularly important in the case when the pool of available experts is limited (or about to be limited in the future).

As has been noted in other works, ‘‘an AI system must be capable of doing three things: (a) store knowledge; (b) apply the knowledge stored to solve problems; and (c) acquire new knowledge through experience. An AI system has three key components: representation, reasoning, and learning’’ (3a).