Monthly Archives: February 2014

Pilot Knowledge Acquisition

Methods of capturing knowledge of both control and naviga­tion activities are varied. Certain things are known about the aircraft that are derived from the engineering process. Other systemic effects, such as the role of the pilot, are less certain, and there is a need to capture expert knowledge. One such method is the use of models that present reasonable represen­tations of the expert.

Multiple model integration is used to reduce the need to explicitly define the knowledge for all cases and creates spe­cific rules that fire in general conditions (environment is de­fined heuristically). This method employs both detailed and general knowledge acquisition and modeling, while yielding high confidence in the rules that fire. Piloting is well suited for such implementations, because the use of procedural knowledge to induce rules can be used to meet the need for specificity, whereas the general environmental conditions may be described using generalizations. The use of concept mapping is another method of reducing knowledge acquisition problems in complex situations. Concept mapping allows spe­cialized knowledge in the form of heuristics and problem-solv­ing methods to be explicitly associated by the knowledge us­ers with static facts and general knowledge (19). Specific to concept mapping is the use of two techniques: first, the use of combing multiple input whereby the experts have collectively generated a summary map of the knowledge required for the particular domain; the second technique is that of indexing, which results in the development of themes and key concepts that emerge from the relationships generated in the summary mapping process.


When exploring pilot communication activities, a number of different communications take place where ES are employed. Pilots receive information from the aircraft systems in the form of displays, and send information to each other and to others on the ground. A remarkably clear pattern-of-informa- tion needs exists during a large percentage of the time pilots are flying. Using this pattern, ES designers have imple­mented systems that anticipate and provide the information needed when it is needed. Typical systems include the auto­mated information and crew alerting systems used to monitor aircraft systems, detect trends and anomalies in the system, and alert the crew to the problem. These are truly ES, in that they gather data and, rather than merely responding to it, they analyze it, consider alternative responses, and then initi­ate action.

These ES are found on most transport and military aircraft and are developed using engineering data to derive functional limits, which in turn support both rule-based and input – driven algorithms. Other forms of ES, which support pilots by managing information, are used to communicate data from the ground to the aircraft. Based on a set of rules developed by industry, a priority is assigned and managed to ensure the timely, accurate, and intelligible delivery of data between the ground station and the aircraft. These ES function on the premise that navigation and environmental information, such as weather reports, may or may not be relevant to the partic­ular aircraft receiving the broadcast. Using rules and real­time information residing in the aircraft itself, the ES moni­tors, acquires, and posts information for the pilot based on the criticality of the information already in the queue.


Navigating an aircraft is sometimes simple and sometimes complex. As with control, a number of ES-type technologies exist to ensure that the task of navigating the aircraft is safe and effective. When coupled with the control computers, the autopilot can take on the task of determining position and guiding the aircraft to its intended destination by returning commands to the autopilot system. This would imply that air­craft are comprised of complex ES networks, which is the case in many advanced turbojet and most military aircraft. Some corporate and general aviation aircraft have similar although less expert systems.

When a pilot of any aircraft is operating in conditions that are favorable for seeing and navigating the aircraft by looking out the window, the use of such systems is less critical. In these circumstances, ES are often employed to monitor and assess pilot navigation performance rather than actively navi­gate the aircraft. In conditions where the pilot relies heavily on ES to navigate the aircraft, such systems are designed to mimic the decision process and control input that would be provided by the pilot. It is common to have an ES that uses both inductive and deductive structures to provide the input to the control algorithms. Most often the system design is re­duced to having a known position and a set of control rules derived for the primary task of controlling (discussed pre­viously) which combine to create a desired effect—the future location. This process is implemented in an ES using deduc­tive reasoning.


Pilots generally understand that aircraft are dynamically un­stable. This is true, even when tendencies toward instability are rendered invisible by avionics designed to override those innate problems. As a result, the human ability to control air­craft attitude is often suspected in control-related incidents. Computing systems supported by expert knowledge about the engineering and dynamics of the vehicle are often employed in these situations. While these may not be purely and exclu­sively expert systems, they provide an excellent, if somewhat oversimplified, example. Flight stability ES use a theoretical approach, coupled with a dynamic input, to create a response algorithm. The use of an optimal control model provides the system with a baseline of engineered performance. The ES designer would typically use a rule-based approach to imple­ment this model. Dynamic input is provided by both the pilot and the flight computers and by inertial reference computers (if available), allowing a control model to be exercised. Often, a sophisticated formula or Bayesian loop is used to control the limits of the autopilot system. Engineers are concerned with the pilot’s induction of out-of-phase or undampened in­put to the system, and thus, in some aircraft, such as those produced by Airbus Industrie, the autopilot system will actu­ally retain and enforce control input limits made by the pilot.

Expert Systems for Pilots

ES in aerospace are often designed to aid the operator of the system, including piloting vehicles. Typically the use of an ES in an operational environment such as piloting can be ori­ented to supporting the pilot or assessing the pilot. Often, ES that are capable of assessment incorporate some form of feed­back or critique to the pilot, further enhancing their use­fulness.

Pilots perform three fundamental activities while flying. The first, and primary task, is control. This relates directly to the pilot’s ability to manage the attitude of the aircraft about the three axes of the aircraft. ES that support control include stability and inertial reference systems. The second activity the pilot performs is navigation. This is control of the move­ment of the aircraft from point A to point B. Pilots are sup­ported by ES such as autopilots and navigation computers. The third activity of the pilot is the need to communicate in­formation relating to the aircraft. ES can provide data link and information fusion based on the pilot’s current and future activities. Knowledge acquisition for these types of systems can be very difficult, costly, and time consuming. Techniques to improve the process include multiple model integration, in­dexing, and multiple input summarization. Examples of sys­tems supporting the pilot are Pilot Associate (17) and Hazard Monitor (18). Both systems are based on real-time data acqui­sition from the aircraft avionics. The system processes the data to create knowledge chunks, used as knowledge base by ES.