Identification of artificial intelligence applications in air traffic control

ow1-26o7:87 u.ca+ Ml 0 1987 Pergamon Journals Ltd.

Trampn. Res.-A Vol. ZIA. No. 1. pp. 27-38. 19~37 Printed in Great Britain.

IDENTIFICATION OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN AIR TRAFFIC CONTROL GEOFFREY D. GOSLING Institute of Transportation Studies, University of California, Berkeley, CA 94720, U.S.A. (Received 11 November 1985; in revisedform 9 May 1986) Ahstmet-Artificial intelligence consists of a broad range of computer science techniques directed at such problems as pattern matching, language processing and solving highly complex, ill-defined problems. The paper briefly reviews the current state of development of the artificial intelligence field and explores how these techniques might be applied to air traffic control. Seven possible control strategies are identified, ranging from visual and electronic collision avoidance, through proposed enhancements of the current U.S. air traffic control system, to strategies in which aircraft follow predetermined, deconflicted flight paths. A large number of possible artificial intelligence applications are grouped into seven func-

tional areas, and ways in which they might be incorporated into the different control strategies are discussed. The paper concludes by considering some of the implementation issues that will arise in the course of applying artificial intelligence techniques to air traffic control

that may be of use to air traffic control. As air traffic volumes increase, existing methods of air traffic control (ATC) begin to impose capacity limits on the more heavily used airspace, while the need for larger numbers of highly skilled controllers results in an increasing cost of operating the ATC system. Faced with the dual problems of increasing capacity while restraining costs, the ATC authorities in both the United States and elsewhere have proposed significantly increasing the level of automation of the ATC system. The U.S. National Airspace System Plan (FAA, 1984a.b) envisages progressively increasing the role of the computerized decision support system through staged implementation of advanced automation features (the so-called AERA system) (FAA, 1981). Other research in Europe is directed at even higher levels of automation (Eurocontrol, 1984). While initial measures to increase levels of automation have been directed mainly at improvements to the controller workstation and measures to improve data handling, it is not surprising that artificial intelligence techniques have attracted considerable interest for longer-term applications (Wesson, 1977; Gosling and Hockaday, 1983,1984; Elias, 1985). This paper describes some of the ways in which artificial intelligence techniques might be applied to air traffic control, and discusses a number of implementation issues that will have to be addressed before such techniques can become an accepted part of ATC technology.

INTRODUCIION

Artificial intelligence is a term that has been used to cover a broad range of techniques in computer science, directed at problems that are not easily solved by traditional, numerical computation approaches. While several definitions have been widely quoted, that by Rich (1983) conveys the essence of the field: Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better. The things to which Rich refers include such problems as pattern matching, language processing and solving highly complex, ill-defined problems. In some ways the term artificial intelligence (AI) is perhaps unfortunate, since it may appear to offer capabilities that do not in fact exist. Intelligence is clearly a desirable quality in many systems, and generally implies the ability to recognize the important aspects of new situations, to reason out what to do about them, and to learn from past experience. Past success at programming computers to perform numerical calculations much faster and more reliably than humans might therefore suggest that AI offers the prospect of developing computer systems that can exhibit the above-mentioned abilities, while operating much faster and more reliably than humans-in short to exhibit superhuman performance. While there are computer scientists for whom this remains a goal, we are presently a long way from achieving it, if indeed we ever will. Dreyfus and Dreyfus (1985) argue that fundamental differences between humans and machines mean that computers, for all their impressive speed and reliability, will always remain shallow imitations of true human intelligence. These reservations notwithstanding, recent advances in AI techniques appear to offer capabilities

THE FIELD OFARTIFlClALlNTELLIGENCE

Before considering how artificial intelligence techniques might be applied to air traffic control problems, it may be useful to briefly review the various areas of AI and their current state of development. 27

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GEOFFREY D. GOSLING

Artificial intelligence deals both with developing general methods for solving problems and with upplication of these methods to specific domains of interest (e.g. medicine), which may be termed AI techniques and AI applications, respectively. AI techniques address such general problems as knowledge representation, search, automatic reasoning, planning and learning. AI applications use these general techniques to program expert systems, natural language processors, speech and vision recognition systems and robots. Knowledge representation is the basis of AI. The way knowledge is represented in a computer implies specific search strategies, reasoning and learning methods. There is no current consensus as to which of the various methods of representing knowledge is better, and it is quite likely that each method is more appropriate for a particular class of problems. Search techniques provide a means to efficiently identify relevant information in a large knowledge base or to examine large numbers of possible solutions to a problem in an organized way. Reasoning techniques utilize search and knowledge representation to develop inferences from given facts. At the top level of AI techniques is machine learning, which uses the lower level methods to enable the computer to enhance its knowledge base and solve problems. AI applications develop from this base of AI techniques. Expert systems provide a way to incorporate the experience of human experts in a computer system that can solve problems in a specific domain, or provide the user with advice. Applications include natural language processing, speech understanding and computer vision. Robotics involves the creation of machines that can perform tasks with some degree of autonomy, and utilizes techniques from all other areas of AI. Today, robotics focuses mainly on planning, problem solving and computer vision; while communication with robots in spoken language should be possible in the future. Each of these areas is extensively described in the many texts that provide an overview of the field of artificial intelligence (e.g. Barr and Feigenbaum, 1981; Rich, 1983), and have been summarized by Hockaday and Okseniuk (1985) from the standpoint of ATC applications. The field of AI is the subject of intense current interest, with the military in particular devoting large sums of money to AI research (Marsh, 1984b), and there are likely to be considerable advances in technical capabilities in the near future. Knowledge representation, search and automatic reasoning techniques are well recognized areas of AI. These techniques have been applied in existing computer expert systems and systems which perform natural language processing. Current work on techniques of knowledge representation, reasoning and search concentrates on: (1) systematizing existing methods; (2) developing new methods for common sense knowledge representation and for approximate reasoning;

(3) developing methods for automatic learning and knowledge acquisition. In the future, methods for knowledge representation and its manipulation will be developed that facilitate reasoning by analogy, and generalization and abstraction of knowledge. The future development of AI techniques will include machine learning and automatic acquisition of new knowledge. These techniques will be incorporated into all other areas of AI. Expert systems appear to be the most rapidly developing area of AI at present. Several well-known operational expert systems have been developed. Among them are MOLGEN for developing molecular genetic experiments, Rl for configuring VAX computer systems to customer’s needs, ADVISOR for interpreting geological information and PUFF for recommending treatment of pulmonary disorders. A considerable number of software tools for developing expert systems are available commercially today. Future development of expert systems will address improved ways of acquiring new knowledge (e.g. from text or real-time data), or methods to improve their performance on the basis of experience. The natural language interface systems that are on the market now are restricted to some subset of natural language. Usually they do not accept grammatically incorrect expressions, and they fail occasionally. Nevertheless, they are considered to be useful systems. Several machine translation systems are also available. They are primitive and do not compare with human translation. They can, however, save the human translator time, by providing rough (and sometimes incorrect) draft machine translations for review and correction. Some commercial systems for isolated spoken word recognition exist, that can recognize words from a relatively small vocabulary with accuracies of up to 99.5%. They usually need to be “trained” to recognize the pronunciation of particular users. Development of systems with much larger vocabularies can be expected in the near future. The problems of recognizing continuous speech, or adjusting to differnt users, are more difficult and will take longer to solve. Context information will be used to parse partially correct sentences. Later, this mechanism will be used in systems that can understand spoken language in strictly limited domains. Computer vision systems are now available that recognize limited sets of objects. Future systems will be able to recognize broader classes of objects and utilize stereo-vision to obtain spatial information. Robotics is another major area of AI research activity, due to the application to factory automation, and is stimulating advances in computer vision and machine learning. Some robots in factory production lines can recognize when objects appear and decide when to begin their activities. Considerable research is also being directed at developing autonomous moving robots. Although AI is a relatively new field, it appears to

Artificial

intelligence in air traffic control

have enormous potential to enlarge,performance in many areas of human endeavor. Ak %ith any major change, there are institutional and psychological barriers to its implementation; nevertheless, many practical applications are available today, and it seems likely that these applications will expand rapidly in the future. APPLICATION

OF Al TECHNIQURS TO AIR

TRAFFIC CONTROL

In attempting to identify potential applications of AI techniques to air traffic control, one may approach the question from two directions, either by examining current and likely future AI capabilities and asking where these might be applied in the ATC domain, or by examining the problems being faced by the ATC system and asking how might AI techniques help resolve these. Both approaches have their strengths and drawbacks. While the first approach sometimes has the appearance of a solution in search of a problem, it may raise possibilities that might not previously have been considered. On the other hand, the second approach appears to offer immediately implementable gains, although it is limited in scope by the characteristics of the present ATC system. As others have pointed out already, to gain the full advantage of increasing the level of automation made possible by the use of AI (and other) techniques, it may be necessary to radically change the way ATC services are provided (Eurocon trol, 1984; Elias, 1985). These changes could involve the navigation and communication equipment required on aircraft, the structure of the ATC organization and division of airspace or the control rules used to maintain separation between aircraft. The existing ATC system is nearly 50 years old, having its origins in the late 1930s with aircraft reporting their positions by radio and air traffic controllers moving markers representing each aircraft on large maps. The advent of radar in the 1940s permitted much closer separations to be safely maintained, but the basic procedures remain unchanged to this day. The introduction of computer “automation” in the 1960s and subsequently has been hugely confined to data management (e.g. flight plan processing) and provision of improved .information on displays. Very little actual “control” is performed by the computer software, even to the level of suggesting options to the human controller. Nor is there much differentiation in the tvpe or quality of control service provided in different parts of the airspace, except that no ATC service is available in some parts or to aircraft not meeting quite restrictive equipment requirement in other parts. While standardization of the ATC system has obvious advantages in terms of training both aircrew and controllers, these advantages are achieved at a very heavy cost. In 1983 FAA projected that the facilities and equipment specified in the U.S. National Airspace System Plan would cost between 8.7 and 9 billion dollars over the first 10 years of the 20-year program (U.S. General Ac-

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count,ipg Gffice,, 1983), a$rge part of which is for rep&_&g ‘c&&ut&‘eqttipt@nt and controller workstations (“sector suites”) at all Air Route Traffic Control Centers (ARTCCs). Yet not all ARTCCs face anything like the same level of traffic density or complexity of airspace structure. Designing the equipment and procedures for the entire U.S. ATC system to meet the needs of, say, the New York terminal airspace is not likely to be a very costeffective solution. This suggests that future AI applications might be implemented selectively, perhaps initially in less critical areas to gain operational experience, then later in those. areas where significant benefits can be realized. The usefulness of a particular technique will depend on the control environment in which it is implemented. Conversely, the availability of particular AI techniques may mean that certain control strategies may become feasible that are not at present possible. Therefore in evaluating the potential applications of AI techniques to air traffic control, a range of possible control strategies should be considered, where each control strategy defines an environment of control rules and procedures and the associated equipment requirements. For the purposes of evaluating the application of AI techniques to ATC automation, Gosling and Hockaday (1984) have defined seven alternative strategies: (1) See and avoid, in which each aircraft is responsible for identifying and avoiding other aircraft through visual contact; (2) Collision avoidance, in which on-board systems monitor the position of nearby aircraft electronically and provide flight crew guidance for evasive action; (3) U.S. Today, in which ground based controllers monitor aircraft with radar, supported by partially automated data processing, and issue clearances by radio; (4) A ERA Stage 1, in which the current system is supplemented by improved communcations and controller support functions; (5) AERA Stage 2, in which the computer would detect and resolve aircraft conflicts, automatically generating the appropriate clearances; (6) Deterministic, in which advanced aircraft flight management systems would permit aircraft to follow approved deconflicted four-dimensional flight paths, with ATC intervention only to handle unplanned deviations; (7) Integrated, in which deconflicted four-dimensional flight plans are adjusted on a real-time basis to respond to changing conditions. The nature of the AI techniques that may be applicable in each of these strategies may be quite different. MOTIVATION FOR DEVRLOANG

AI APPLICATIONS

In view of the extensive amount of computer software that has already been developed for ATC ap-

GEOFFREYD. GOSLING

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plications, using more conventional programming techniques, and the established tradition of applying operations research techniques to ATC problems, it is relevant to ask what AI techniques may have to offer that existing techniques do not. There.are at least two answers to this question. The first is that we will not know until we gain more experience developing such applications. We do know that ATC problems are often complex and illdefined, and it is not clear how successful conventional approaches will be at solving them. Only by identifying application areas and using AI techniques to develop prototype software which can be compared with the more conventional software will we learn how useful these techniques really are. The second answer is more empirical and circumstantial. We observe applications of AI techniques in widely different fields that show varying success and note the similarity of some of the problems being addressed to those that exist in ATC. We also note the difficulties that are experienced adapting conventional techniques to some of these ATC problems. These observations are not surprising because, after all, AI is the study of how to solve these sorts of difficult problems on a computer. Nor should it surprise us that some of the techniques we find under the heading of “AI” have already been used in some of the “conventional” approaches. The field of AI has been around since the early days of digital computers and AI researchers have been trying to program computers to perform basic human skills, skills possessed by the creators of the conventional software. What AI research has done (and is continuing to do) is to provide a framework within which these techniques can be developed, formalized and made available to applications programmers in a relatively structured way, by means of established software tools and techniques. Thus AI techniques should not be viewed as a radical departure from conventional software approaches, but rather as a set of techniques that have application to the particular class of problems discussed above, involving knowledge representation, searching, learning, etc. Identifying where in the ATC system this class of problems occur is addressed in the remainder of this paper. To the extent that existing AI applications often utilize software techniques that are not generally part of conventional approaches, such as symbolic computation or list-processing languages, there may well be difficult issues to be addressed in the integration of these techniques into a conventional software environment.

IDENTWICATION

OP POTENIIAL

APPLICATION

AREAS

In general, ATC systems provide a number of quite different functional areas within which AI techniques may be applied. These include

(1) short term, or tactical, control of aircraft movement; (2) longer term, or strategic, management of traffic flow; (3) improve displays and information management; (4) system configuration management; (5) failure management and exception handling; (6) controller, maintenance and supervisory personnel training; (7) aircraft on-board equipment.

Although the first area tends to be the one that most people think of when considering ways in which AI techniques can be applied to ATC-an automated system issuing bursts of digitally coded instructions directly to aircraft under control-it is likely that the other areas will prove more fruitful for applying AI techniques, at least in the foreseeable future. While in principle a fully or partially automated control system can be postulated, as has been done by Eurocontrol in their ARC 2CKIO proposal (Eurocontrol, 1984) or by the FAA with the proposed AERA system (FAA: 1981,1984a, 1984b), the implementation difficulties, some of which are discussed below, will be extremely severe. It is likely to be much easier to initially implement AI techniques in those areas that are not directly concerned with the maintenance of safe separation between aircraft. However, one potential application of AI techniques to tactical control of aircraft that may produce significant benefits in the short term is the implementation of intelligent assistance to controllers. This might consist of a package of software functions utilizing expert system techniques that would operate in conjunction with the existing radar display and flight data processing software to assist the controller by improving the presentation of information, issuing alerts for required actions, generating recommendations and providing a capability for controllers to execute routine functions automatically. Such a package could significantly reduce controller workload by reducing the amount of time that the controller needs to spend performing routine tasks, and enhance system safety by supplementing the controller’s monitoring function. It is well known that monitoring complex systems for potential problems is a task that humans do not alwaysdo well. Attention levels drop during periods of inactivity, and it is easy for controllers to become distracted by a problem in one part of the system and fail to notice a second, perhaps more serious, problem developing elsewhere. There is also the cognitive problem that humans tend to interpret what information they receive in the light of what they expect to observe on the basis of their current perception of the situation. This can lead to problems of misinterpretation or misunderstanding. A large number of potential applications of AI techniques to different ATC functions have been

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Artificial intelligence in air traffic control

identified by various studies, and are summarized in Table 1. Each of these areas is discussed in more detail below. 1. Tactical conhol of aircraft movement Attempts to identify ways in which AI techniques can he used to assist human tactical controllers have

addressed both the provision of enhanced support functions as well as the automation of routine tasks. These techniques might be combined in a “controller associate” -an expert system that could help reduce controller workload and enhance safety by operating in parallel with human monitoring and decision-making. The expert system could track aircraft positions and alert controllers to potential decisions that may have to be made, based on a representation of both the controller and flight crew planning process. Increased use of advanced flight management systems on aircraft, with the capability of flying fuel-efficient, 4D flight paths, will increase the complexity of the tactical controller’s task and may lead to much greater variation in the flight paths that aircraft request or fly. As an extension of this function, conflict probes need to be developed that can look downstream into the next sector and beyond, in order that controllers can organize traffic efficiently and issue appropriate clearances. lltese probes need to be coordinated with

decisions being taken in other sectors and should anticipate Iikeiy future d,“c$ms by other controllers and flight I&&, as web as consider weather changes and sector workload requirements. The combinatorial explosion of possible situations suggests that exhaustive search techniques are not appropriate and that heuristic search or rule-based systems are more promising. Other applications of intelligent assistance to reduce workload might include generation of menus of alternative actions with recommendations, evaluation of controller generated alternatives and automatic execution of routine functions such as hand-offs. In the longer term, as experience is gained with using AI techniques to generate solutions to tactical control problems, certain decision-making could be fully automated, with human control reduced to a supervisory function or intervention to handle exceptions (such as aircraft in distress). It would appear reasonable that such techniques might be applied initially in lightly loaded sectors, allowing controllers to direct their attention to more complex problems. A somewhat different application is the concept of flexible control rules that vary in response to the situation, rather than rely on set standards that have to be learned abead of time. Advanced graphical techniques would have to be developed to indicate

Table 1. Potential applications for AI in ATC Area 1.

Tactical control of aircraft movement

Application Alerts for potential decisions Extended and coordinated probes Menus of alternative actions with recommendations Automatic execution of routine functions Automated decision-making Flexible control rules

2. Strategic management of traffic flow

Traffic routing Improved interface with tactical control Fuel analysis as part of control decisions Deconflicted 4D flight plan generation Demand responsive scheduling Aircraft delay allocation Airport capacity forecasts Improved presentations of information

3. Improved displays and information management

Automatic clearance transfer Voice synthesis and recognition

4. System configuration madagement

System configuration planning Runway and airspace configuration management System monitoring and crisis anticipation Contingency planning

5. Failure management and exception handling

Failure recovery support and system Configuration selection System restoration Major disruption response

6. Personnel training

Improved simulation techniques Pseudo-pilot automation

7. Aircraft on-board equipment

Collision avoidance direction Intelligent checklists Procedure monitors Computer vision

GEOFFREYD. GOSLING 32 the extent of the rules, with perhaps protected airclearances. Acknowledgment of the clearance could space around an aircraft shown on the radar scope be registered by the controller in the same way. If in color. Thus the controllers might operate accordunacknowledged after a set interval the clearance ing to a set of meta-rules, for example “keep aircraft could be automatically reissued. The system would out of each other’s protected airspace,” rather than need to monitor the voice channel and recognize a “keep aircraft X miles apart.” break in communication to commence transmission. The introduction of digital data links provides the 2. Strategic management of traffic flow opportunity to significantly improve communicaThere is increasing interest by ATC agencies in tions between aircraft and controller. In particular, resolving capacity problems by better regulation of messages can be “posted” on a text display for later traffic flow and routing, rather than simply improving reference, reducing errors of interpretation or recall. the capability of handling whatever shows up. Since However, both controllers and pilots have lots of strategic control decision-making is generally being other things to look at, and a voice supplement reperformed earlier in the process and under less prespeating critical messages may be a valuable addition. sured conditions than tactical control, while the probThere is also the “party-line” problem, whereby pilems being addressed are generally more complex lots rely on overhearing messages to other aircraft to and less well-defined, there appears to be considerbuild a mental picture of the surrounding traffic. To able potential for expert systems applications. avoid cluttering the message display with commuThese include traffic flow routing to take account nications between ATC and other aircraft, these could of airspace capacity and sector workload constraints, be abstracted and voice synthesized. In the future, as well as weather conditions. Improved interface more sophisticated systems might be able to select with the tactical control positions would be able to which messages to and from other aircraft were critanticipate developing probkms and route traffic flow ical and only repeat those. or delay flights to relieve capacity or workload constraints. For longer-term flow control, schedules and 4. System configuration management flight plans might be assessed and revised, based on The ATC system configuration is modified on both improved understanding of existing capacity cona short-term and long-term basis. In the short term, straints; and a set of deconflicted 4D flight plans changes in wind direction, traffic mix and facility could be developed to minimize the need for tactical availability influence decisions on which runways to control. Where delays must be imposed on aircraft, use and how to staff sectors or route traffic. In the these could be done in an intelligent manner that longer term, airspace can be resectorized, air routes recognizes downstream consequences for the airchanged, navigation aids installed or moved and concraft, such as the ability to make scheduled connectrol responsibility reassigned to different facilities. tions or the arrival time at a second capacity-conThese decisions require considerable expertise, and strained airport. are frequently made on a trial-and-error basis, or in Improved forecasts of runway capacity could be response to a system failure. It would seem that exdeveloped that better recognize airport specifics and pert systems could be developed to assist shift suuncertainties for braking action, weather and other pervisors, tower chiefs or airspace planners. factors. A learning capability could be built in that allows historical data to be assessed and the forecasting logic to be improved continually. 5. Failure management and exception handling Design of the ATC system must provide backup 3. Improved displays and information management capability to ensure the continued functioning of the Improvement in the presentation of information system under conditions of component failure or erto controllers is one area where substantial gains might ror. However, it is precisely under these conditions be made in the near term, and is the subject of intense that the system is likely to be at its most stressed, research and development activity as part of the FAAs with heavy workloads on the controllers and system sector suite procurement program. managers, as they attempt to perform with limited Application of AI techniques could include autoresources or abnormal procedures. Once the immatic configuration of diaplays to respond to the mediate crisis of the failure or error has passed, the current situation, highlighting of critical decisions and system managers must establish a stable operating the display of alternative (or recommended) courses situation until restoration of the normal environment of action. While improvements in display technology can commence. Expert systems may provide valuable do not necessarily require “intelligence” to be effecsupport under these conditions, given the limited tive, problems can arise with information overload experience of the system managers with any given or displays that are too elaborate-an effect that has failure condition, and the other pressures they are arisen with some aircraft cockpit electronic flight dislikely to be under at the time. plays, and that pilots have come to term the “Atari There appear to be a number of potential applieffect” (Wiener, 1985). cations in the area of failure recovery management, Voice synthesis could be used to reduce controller where such support could be utilized: workload by permitting push-button issuance of flight (a) Monitoring the normal functioning of the sys-

33 Artificial intelligence in air traffic control ATC system itself, and system managers may require tern to identify developing situations that might lead similar decision sutmort aids. to a crisis if a component should fail er an operational error occur. System managers could be alerted to the 6. Personnel training nature of the potential crisis, so that they can forTraining and certifying personnel to operate and mulate contingency plans, increase supervision to remaintain complex control systems are time-consumduce the likelihood of error or unload part of the ing and expensive tasks. Expert systems developed system to reduce the threat. Current work in progress for operational use can be used to enhance training for the FAA has suggested that it may be possible to activities, either in conjunction with simulation exanticipate certain types of hardware failure by monercises or in computer-aided instruction. The capaitoring the performance of particular system funcbility of expert systems to display the reasoning proctions. ess behind a conclusion is particularly valuable as an (b) Developing contingency plans that could be instructional tool. The combination of expert systems offered to system managers in the event of a particand speech processing could be used to replace huular failure, to enable them to rapidly implement the man pseudo-pilots in real-time system simulation, recovery process. Given the large number of possible reducing training costs and releasing personnel for courses of action to cope with the situation, the camore productive duties. pability of an expert system to dialogue with its user and explain the logic behind a recommendation could 7. Aircraft on-board equipment be particularly valuable to the system managers, both Considerable attention is currently being directed to reassure them that certain factors have been conat aircraft cockpit applications of AI by the U.S. Air sidered and to inform them of facts that they might Force, the Defense Advanced Research Projects be unaware of or have overlooked. Agency, and others (Gregory, 1984; Stein, 1985). (c) Providing real-time support for system managers as they attempt to redeploy their resources to Cockpit functions can be considered to fall into three cope with a failure. This support could either be in categories: aircraft systems operation; navigation, the form of automated support of certain functions collision avoidance and ATC compliance; and misto permit controllers to handle more traffic than would sion performance. Although AI may be applicable otherwise be possible, albeit at the cost of some tradeto all three categories, this paper is primarily conoffs such as efficiency or flight time, or to assist in cerned with the second. establishing the failure recovery process, given the Voice and digital data link communications mancurrent capabilities of the system. In any complex agement applications have been discussed above. The system, it is unlikely that every component will be Traffic Alert and Collision Avoidance System (TCAS) functional at any one time, since some failures (even system currently under development (Boucek, et al., minor ones) may have already occurred and other 1985) could benefit from the application of AI techcomponents may be off-line for routine maintenance. niques to extend the search and analysis activities to Furthermore, the capability of any other part of the cover multiple aircraft conflicts, larger time frames system to handle reassignment of functions will deand larger menus of alternative actions. Intelligent pend on the current traffic level it is handling. Therechecklists that respond to the current situation and fore the failure recovery procedure is likely to be a script-based procedure monitors that alert flight crews complex timedependent decision, in which an expert to deviations from standard procedures could be used system could guide the system managers in estabto reduce flight crew workloads and enhance safety. lishing the system configuration during the recovery Computer vision sensors, coupled with pattern recprocess. ognition, may in the long term be able to supplement human vision in see-and-avoid situations. Use of (d) Providing support during system restoration to normal operating mode following a failure. Once the wavelengths and sensors outside the visible spectrum might extend VFR capabilities into some instrument failed components have been repaired or replaced, meteorological conditions. it is necessary to determine how to restore the system from the condition it is operating in during the failure to its normal operating environment, including the APPLICATION TO DWFRRRNT CONTROL STRATRGIRS timing of any reallocation of control responsibilities As discussed above, the potential usefulness of any and restoring or updating any computer data bases particular AI techniques will depend on the control lost in the failure. These functions have to be perstrategy in which it is implemented. Likewise, the formed while controlling traffic in real-time. effectiveness and applicability of a particular control In addition to failures of the control system itself, strategy may depend on the extent to which particthe ATC system has to be able to respond to major ular AI techniques can be incorporated. It appears disruptions to the air transportation system that are that artificial intelligence techniques may provide a caused by events such as closure of a major airport quantum change in the potential for incorporating due to weather. Because of the highly tactical nature computer support into control system automation, of the problem, at least in the first hour or two after radically restructuring the human/machine relation. the disruption, the impact on air traffic flow bears a As with all such changes, the true potential lies not strong similarity to that resulting from a failure in the

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GEOFFREYD. GOSLING

in doing the same old thing in a slightly better way, but in taking advantage of the opportunity to achieve order of magnitude improvements by approaching the problem in a quite different way. The following sections describe how various AI techniques might be incorporated into the seven alternative control strategies discussed above. While many of the potential applications suggested may require considerable increases in computer processing capabilities, the current pace of development indicates that improvements of several orders of magnitude may be expected in the foreseeable future. At the same time, the necessary software techniques will require extensive research and development, and many of the suggested applications could take years or decades to realize. Strategy l-See and avoid In this strategy there is no tactical control of individual aircraft, which operate according to visual flight rules (VFR). AI-based systems could be used to monitor filed flight plans or radar data to identify times and places where either this strategy is effective, or where risk is high enough that the strategy is inappropriate. Expert systems could be used to significantly enhance the quality of the service and level of automation of Flight Service Stations. An expert system might be developed that could assess historical data on traffic density and characteristics on different routes, assess current and forecast weather, search alternative routes between origin and destination for particular aircraft and identify trade-offs between fuel consumption, flight time and risk. Based on an internal representation of the aircraft operator’s vahre system for the relative importance of different measures of effectiveness, the system could recommend a preferred route and respond to questions concerning the rationale and alternatives. Reports of routes flown could provide a data base for improved decisions on future flight plans. Heuristic mles could be developed to limit the route search process, utilizing a knowledge base derived from interviews with pilots, controllers, flight service personnel, simulation and/or case histories. Computer vision offers some interesting possibilities. While it appears unlikely that computer vision technology will approach human performance in the foreseeable future, if ever; the development of a reliable visual pattern recognition technique, combined with low-cost sensor technology, could significantly improve the performance of see-and-avoid techniques. The system would be in operation continously, in contrast to the pilot who must also devote time to aircraft control and navigation, and could scan directions obscured from the pilot. Sensors located in several locations around the aircraft could provide a level of visual surveillance not possible today and would not require other aircraft to have any special equipment in order to be detected. These sensors might operate in several wavelengths, including visual and infrared and also provide stereo-

scopiccapability. Whether such an application would ever be cost effective compared with other traffic alert technologies would depend on the relative costs of the sensors and data processing requirements. Although AI is usually considered in the context of positive control (IFR), it also appears to have the capability of significantly improving both the safety and efficiency of VFR flight. Strategy 2-Collision avoidance The see-and-avoid techniques discussed in Strategy 1 rely first on visual identification of other aircraft and then on pilot action to resolve any conflicts. Replacement or supplementation of visual reference by electronic scanning and/or replacement or supplementation of pilot conflict resolution by automatic conflict resolution are all amenable to AI applications. Several alternative technologies are available for electronic identification of aircraft, and these open the opportunity for extending see-and-avoid types of control strategy into instrument weather conditions. Whether based on radar, radio transponders, satellite position finding or other techniques, these technologies provide information on the relative speed and position of nearby aircraft. Heuristic searches could be conducted to identify and select aircraft trajectories that satisfy stated measures of performance including the minimbation of collision risk. Such searches appear to be feasible with AI techniques available today or in the near future. Research is underway at NASA and other locations to develop techniques for cockpit display of traffic information (CDTI) based on a variety of sensors, including ground based radar. The appropriate display of complex information in a noncritical situation and possible resolution strategies appear to be areas where AI techniques could be applied. Future TCAS development offers an opportunity for use of AI in implementing automatic instructions when needed to avert a collision. Current forms of TCAS prototypes provide only vertical evasion guidance from a single conflicting aircraft. AI might provide the basis for more sophisticated systems that provide a wider menu of alternative evasive maneuvers and that consider more than one aircraft. Strategy 3-U.S. today It is clear that the current ATC system, which emphasizes tactical human ground-based control, could benefit from some AI techniques. Each human control function that uses some form of computer assistance today can benefit from AI. In addition, some functions that are totally manual today could benefit from partial automation that involves AI. The capacity of the current ATC system is often limited either by controller workload or by uncertainties concerning aircraft status and intentions. Further automation of data support functions and inclusion of additional predictive capability and menus of alternative control measures would help alleviate

Artificial intelligence in air traffic control

both the workload and the uncertainties. AERA (see Strategies 4 and 5 below) represents one approach to this problem. One alternative would be to provide suggestions for control instiuctions directly on the existing display, perhaps in the forms of graphical information such as a moving box that represents a target for the controller to follow or suggested control phraseology to achieve desired flight paths. Knowledge representation techniques could facilitate development of appropriate phraseology, and heuristic-based search techniques could facilitate the selection of appropriate graphical information. Another possibility is the use of voice recognition and speech synthesis techniques to reduce the communications workload on the controller, or reduce the ambiguities of voice communications by providmg a redundant data link. These technologies appear to be developing at a rate that could make automatic voice links for the limited jargon of ATC feasible in the foreseeable future. AI could also be helpful in tailoring computer sup port functions to the needs of a particular control position. It is possible to envisage a wide range of such support functions which might include profile or 3D displays; color displays that indicate aircraft speed, altitude, etc.; menus of alternative actions; data on upstream and downstream sector conditions; and automatic hands-off execution. Machine leammg and expert systems techniques could be combined with cognitive models of the control task to vary the support functions in response to changing conditions and controller preferences. Runway configuration management (the selection of appropriate runways for arrivals and departures) 1scurrently performed manually. Development work has been performed on a prototype automated configuration management system for Chicago O’Hare tntemational Airport. Response to real-time variations in weather and demand is problematical, and expert systems may help in producing satisfactory solutions. It is clear from the above examples that several AI techniques might be applied to improve the current ATC system, without necessarily evolving along the path towards any of the strategies described below. Strategy 4-AERA Stage 1 This initial stage of AERA involves the use of algorithms that probe along potential aircraft trajectories and test for con5icts with other aircraft. One limitation of this technique is that probes into adjacent sectors are not coordinated, and therefore airspace may be allocated (by the probe process) that appears vacant but in practice is also planned to be used by aircraft either already in the probed sector or from a third sector. In addition, the probe examines only one prespecified route, rather than a menu of alternative routes that have similar performance characteristics. Extensions of current AI techniques are apparently capable of expanding the search

35

by the conflict probe, and also coordinating probes and flight paths between sectors. Fuel &i&iti@tioti +&&ns could be built into the strategic planning activities, for example, to produce recommended conflict resolutions that meet prespecified fuel-based criteria. The interfaces of AERA and the air route traffic control centers with other centers, terminals and central 5ow control could all be improved by using AI techniques. Control actions that conform with external constraints such as metering rates could be recommended automatically. Strategy 5-AERA Stage 2 The second stage of AERA involves additional assistance to the controller in the form of automatic resolution of some traffic conflicts. The details of the required software for AERA Stage 2 have yet to be developed, and it appears that AI techniques could have a large role to play in making Stage 2 feasible. In particular it is likely that the controller will not accept AERA Stage 2 control recommendations unless it is possible to interrogate the software and obtain reasons why the recommended action is preferred over other alternatives (including one that may be preferred by the controller). An alternative is for the controller to ask the computer to evaluate a spe cific controller generated solution, to identify any unanticipated weaknesses. Expert systems have the capability of providing this dialogue, and heuristic searches may be used to help the expert system examine a reasonable set of alternative control actions. Strategy &-Detemindic In this strategy, 4D flight plans are reviewed and approved that do not con5ict with other aircraft. Aircraft must then follow the approved plan within specified tolerances, and conformance monitoring is performed en route. The selection on a national basis of a set of 5ight plans that are decon5icted from each other is a complex process with an infinite set of alternatives. A search must be performed that both considers a reasonable set of alternatives and assesses the measures of effectiveness for each flight. In addition to the primary set of 4D flight plans, alternate (backup) night plans must also be developed to permit the safe handling of nonconforming aircraft. The development of the primary set of Bight plans must be done in a way that permits the creation of feasible alternative flight plans for every aircraft. Algorithms for such a search do not exist today and may be difficult to develop. AI techniques that limit the scope of the search while still attaining satisfactory measures of effectiveness are available in elementary form, but much detailed development would be necessary. The most difficult part of the development would be in the area of machine leaming. It would be desirable that experience from previous days of flight (from real world data or from simulations) be used to improve the efficiency of future plans.

GEOFFREY D. GOSLING

36 Strategy 74ntegrated

This strategy extends the deterministic concept by permitting updates to flight plans during the conduct of a flight as conditions warrant. This strategy has the potential for higher measures of effectiveness than the deterministic approach, but would require significant additional computational power. The AI-based techniques needed for the deterministic strategy would also be directly applicable to the integrated approach. The difference is that, in lieu of an accumulation process where new flight plans are added to an existing fixed set, the complete set of active flight plans may be reviewed and updated on a frequent basis. Heuristics (preferably based on machine learning) would be fundamental to keeping the required computations at a manageable level. For example, they might be used to limit the geographic area, number of aircraft, time span and/or alternative paths to be investigated. IMPLEMENTATlON

CONSIDERATIONS

It is one thing to review the current state of the art of artificial intelligence and propose applications where these techniques appear to offer potential benefits, and quite another to implement such techniques in a complex, real-time control system. Experience in applying expert systems in a commercial or military environment suggests that systems that work well in a laboratory environment suffer severe degradation in their initial performance when placed in the field, and that considerable subsequent effort is required to enhance their capabilities to function effectively under operational conditions (Taylor, 1983). While this may be acceptable for off-line systems, or systems that can be rehearsed in noncritical, live drills (such as military war games), there are obvious problems for real-time control systems, such as the ATC system. Even in such off-line applications as a traffic flow management advisor, there are significant workload and training burdens that will be placed on the operational staff in order to interact with the system, while doing their regular job. This will be a particularly severe problem if development of the system is expected to continue after it has been installed (as is almost always the case with AI applications). If this aspect has not been properly considered in developing the implementation and evaluation program, and adequate resources made available to system development, the likely outcome will be dissatisfaction with the new system by the operational staff, as the novelty wears off and rising expectations and needs are not matched by increasing system capabilities. Eventually this dissatisfaction may lead to the system being ignored or abandoned, as happened with early attempts to provide expert systems support in the management of aircraft carrier operations (Roberts et. al., 1985). In order to develop and implement practical applications of artificial intelligence in an ATC system

a coherent research program is required that not only addresses the technical issues of how to build the relevant software, but also explores the issues that must be addressed in order to implement this software in the real world system. Among the questions that such a program should address are: (1) What is the appropriate role of the human controller in an automated control system, and what decision-support aids, responsibilities, training and qualifications should such a controller have? (2) How is computer software to be tested and certified before it is placed into service to control live traffic, and who is responsible if such software subsequently fails? The question of legal liability for the consequences of failure of computer software is likely to become a major concern that will limit the type of applications that are considered. (3) How much is formally known at present about how controllers and other ATC personnel actually do their jobs on a day-to-day basis, and how can this knowledge be represented for use by software designers developing expert systems or other applications? (4) How well-specified are questions concerning the future evolution of the ATC system, and what additional research is needed to better specify the problems that will confront the system beyond the 1995 time frame? (5) What further research into artificial intelligence and related techniques is required in order to develop the capability to actually build AI modules for use in ATC system software? (6) How are trade-offs to be made between conflicting objectives of the ATC system, such as cost versus safety or capacity versus reliability? In fact, none of these issues are unique to air traffic control systems, and similar questions are likely to arise in other applications of AI techniques to realtime control systems. This suggests that those responsible for developing ATC research programs should not allow the obvious differences between air traffic control and, say, nuclear power plant operation to obscure the similarities in the problem being faced and the opportunity to learn useful lessons from the experience of those systems. CONCLUSIONS

Artificial intelligence techniques appear to offer a wide range of possible applications to air traffic control under various alternative control strategies. Of the various branches of artificial intelligence, it ap pears that techniques developed for heuristic search and the creation of expert systems offer the most promise for near-term application. Areas such as speech interpretation and computer vision may offer potential applications for control of air traffic, but operational systems of the necessary degree of ro-

37

Artificial intelligence in air traffic control bustness are not likely to be available for some time. Potential applications may exist in #& tfaining and research environment in a much closer time frame. Alternative control strategies that can be identified offer a wide range of both performance and implementation cost. It appears reasonable to assume that different strategies would be appropriate under different circumstances, particularly under different levels of traffic density. Consideration should also be given to the magnitude of the incremental benefits from more sophisticated control systems, in relation to the incremental costs. Thus the choice of the development path for a future ATC system should address what mix of strategies is most appropriate under particular circumstances, rather than which particular strategy should be selected for application to all situations. The extent to which artificial intelligence techniques can be introduced into the various ATC strategies varies, but there are potential applications in every strategy considered. Although the U.S. National Airspace System Plan has identified a program of increased automation of the ATC system, represented by two of the seven strategies considered in this paper, several interesting potential applications appear to exist under strategies with considerably less automation, or even without direct control of air traffic. In view of the much larger number of general aviation aircraft than air carrier aircraft, and the generally lower levels of pilot experience and ability, the potential safety benefits of relatively low-cost applications in areas outside positive control airspace appear to deserve closer examination. In addition to applications involving support for the real-time control of air traffic, there appear to be several important applications in other aspects of the ATC system, particularly for expert systems. These include support for failure recovery management, centralized strategic planning (central flow control), controller and support staff training and airspace configuration plarming. These applications have the advantage that, not involving direct control of aircraft, they may be easier to implement on an experimental basis. The concept of an intelligent “controller’s assistant” module, as part of the evolution of the ATC system, offers an opportunity for several AI applications. The combination of expert systems techniques and advanced graphic dispiay capabilities could assist in reducing both controller workload and the potential for error. Application of AI techniques to existing features, such as conflict alert, may allow considerable increase in sophistication, while reducing the disruption caused by false alarms. One aspect that should be recognized is that to achieve the full benefits of applying AI techniques, it may be necessary to significantly change either procedural rules or the way ATC services are provided. Merely trying to layer such techniques on top of an existing system in an incremental fashion may at best prevent many of the potential benefits from being achieved, and at worst produce a system that

is so cumbersome and complex that its performance is act&&~ worse than b&r@@ Thus implementation issues are every bit as important as technical issues. The design of AI applications should take account of how the particular application will be integrated into the ATC system. This aspect appears poorly understood and is an area that requires considerable research if the full potential benefits of AI techniques are to be achieved. REFERENCES

Barr A. and Feigenbaum E. A. (1981) The Handbook of Artijicial Intelligence, Vols. I, II. Heuristech Press, Wil-

liam Kaufman Inc. Boucek G. P. Jr., et al. (1985) Traffic alert and collision avoidance system: Operational simulation. U.S. Federal Aviation Administration Report No. DOT/FAA/PM-IS/ 10. Boeina Commercial Airnlane Comnanv. Seattle. WA. Dreyfus H. &d Dreyfus S. (1485) Mind bve;Machine: The Power of Human Intuition and Expertise in the Era of the Computer. The Free Press. Elias A. L. (1985) Rule-based implementation of automation aids for ATC controllers. Report R-85-1, Flight Transnortation Laboratotv, Massachusetts Institute of Technology, Cambridge, MA. Eurocontrol (1984) Automatic Radar Control 2ooo: A demonstration system for air traffic control of 21st century. Program description, Eurocontrol Experimental Center, Bretigny sur Orge, France. Gosling G. D. and Hockadav S. L. M. (1983) Control strategie; for automated co&o1 of transportation systems. Research renort UCB-ITS-RR-83-5. Institute Transnortation Studi&, University of California, Berkeley, CA. Gosling G. D. and Hockaday S. L. hi. (1984) Identification and assessment of artificial intelligence techniques in air traffic control. Research Report UCB-ITS-RR-84-14, Institute of Transportation Studies, University of California, Berkeley, CA. Gregory W. T. (1984) Another cockpit revolution. Aviadon Week Space Technol., Dec. 10. Hockaday S L. M. and Okseniuk A. M. (1985) Artificial intelligence and its potential application to air traffic control. Working Paper UCB-I’I’%WP-85-10. Institute of Transportation Studies, University of California, Berkeley, CA Marsh A. K. (1984a) NASA to demonstrate artificial intelligence in flight operations. Aviation Week Space Technol., Sept. 17. Marsh A. K. (1984b) Pace of artificial intelligence research shows acceleration. Aviation Week Space Technol., Dec. 10.

Rich E. (1983) Artificial Intelligence. McGraw-Hill, New York. Roberts K. H., Boettger R., Scott D. W. and Sloane S. B. (1985)TheU.S.S. CarlVinson: A history andevaluation. Working Paper, School of Business Administration, University of California, Berkeley, CA Shivelv C. A. and Schwamb K. B. 11984) AIRPAC: Advisor for the intelligent resolution‘ of predicted aircraft conflicts. Report MTR84W164, The MITRE Corporation, McLean, VA. Stein K. J. (1985) Expert systems research focuses on combat emergency procedures. Aviution Week Space Technol., Oct. 28. Taylor E. C. (1983) Knowledge engineering: making artificial experts. TRW Electronicaand Defense Sector. Quest, Summer. U.S. Federal Aviation Administration (1981) The AERA concept. Report No. FAA-EM-813, Washington, DC. U.S. Federal Aviation Administration (1984a) National

GEOFFREYD. GOSLING

38 AirSDaCe Svstem Plan: Facilities, equipment

and associ-

ated’develipment. Washington, Dd. _ U.S. Federal Aviation Administration f1984b) National Airspace System Plan: Engineering aid development. Washington, DC U.S. General Accounting Office (1983) FAA’s plan to improve the air traffic control system: A step in the right

direction but improvements and better coordination are needed. Washin$on, DC Wesson R. B. (1977) Problem-solving with simulation in the world of an air’traffic controller: Ph.D. dissertation, University of Texas at Austin. Wiener E. L. (1985) Beyond the sterile cockpit. Human Factors 27(1);75-90.