An overview of research in the areas of aerospace applications of artificial intelligence, expert Systems, neural networks and robotics is presented. Challenges associated with aerospace projects require increasingly complex aerospace Systems which in turn demand automation and fault tolerance. We have addressed these issues and provided a survey of the research on intelligent Systems that has been carried out in an attempt to meet these challenges. The application areas we have overviewed include fault monitoring and diagnosis, generation and management of power in space, efficient and effective command and control, operations and maintenance of space stations, planning and scheduling, automation, and cockpit management.

This paper reviews the current state of the art in natural language access to databases. This has been a long-standing area of work in natural language processing. But though some commercial systems are now available, providing front ends has proved much harder than was expected, and the necessary limitations on front ends have to be recognized. The paper discusses the issues, both general to language and task-specific, involved in front end design, and the way these have been addressed, concentrating on the work of the last decade. The focus is on the central process of translating a natural language question into a database query, but other supporting functions are also covered. The points are illustrated by the use of a single example application. The paper concludes with an evaluation of the current state, indicating that future progress will depend on the one hand on general advances in natural language processing, and on the other on expanding the capabilities of traditional databases.

This review provides an overview of the present state of the art in applying AI techniques to the work of Chemical and Process Engineers. Such work is described, where necessary, in order to make this paper understandable to an audience outside the process engineering community. The first section discusses the nature of chemical engineering design and draws the important distinction between process design and plant design. The complete design cycle is covered a stage at a time and AI applications and issues identified. The section finishes with a discussion of integrated or concurrent design, which is the ultimate aim of much current research. The second section discusses AI applications in process operation. This covers process monitoring, alarm processing and fault diagnosis. The third section discusses, in some depth, design stage loss prevention, that is the design of plants to be safe and reliable. Safety is an integral part of design, but the inclusion of a separate section is appropriate as the qualitative and judgemental nature of many safety related problems has encouraged the use of AI techniques. Applications of AI planning to the problem of automatic operating procedure synthesis and applications of qualitative simulation to chemical engineering problems are also discussed in this section.

Mechanical engineering design is a broad subject area covering many topics and bas influences upon many other engineering disciplines and activities. Computer support for mechanical engineering design activity has been in draughting Systems and analysis packages, but there has been little in conceptual design assistance. This paper presents a number of areas of work in which AI techniques and developments are being used, sometimes in conjunction with traditional methods, to improve the support of design. The approaches to design and design Systems are covered, along with some techniques that are used. Specifie design Systems illustrate progress, and integration issues and simultaneous engineering Systems indicate the way research is moving. Finally, discussion of the trends and future topics indicates where and how effort may be applied in the future.

This paper explores the implications of research results in behavioural decision theory on knowledge engineering. Behavioural decision theory, with its performance (versus process) orientation, can tell us a great deal about the validity of human expert knowledge, and when it should be modelled. A brief history of behavioural decision theory is provided. Implications for knowledge elicitation and representation are discussed. An approach to knowledge engineering is proposed that takes into account these implications.

Expert systems technology has been around for a long time, becoming increasingly easy to use, inexpensive and reliable in recent years. It would seem to provide an ideal vehicle for the dissemination of expertise in developing countries, particularly in the field of medicine, which was the focus of much early work in diagnostic systems. Despite the apparent match of a real problem and a credible solution, however, remarkably few AI systems for medicine in developing countries have been researched, designed and implemented. This paper addresses why this might be the case, reviews some of the extant systems and explores some of the design issues. Particular emphasis is placed on the question of “Appropriate Technology”. Various criteria for Appropriate Technology are explored, and an optimal set used to guide principles of design. It is argued that medical AI systems can satisfy these criteria, provided that sufficient care is taken in their design for the country of application.

Research on planning in AI can be separated into the two major areas: plan generation and plan representation. Most AI planners to date have been based on the STRIPS planning representation. This representation has a number of limitations. Much recent work in plan representation has addressed these limitations. It was shown that Decision Theory can be used to remove a number of the limitations. Furthermore, the decision theoretic framework provides a precise definition of rational behaviour. There remain open questions within decision theory regarding belief revision and causality. It should be noted that these problems are not artifacts of the representation. Rather they arise because the rich representation allows their formulation. Some work integrating AI and decision theoretic approaches to planning has been done but this remains a largely untouched research area.

We see two main avenues for fruitful research. First, the straightforward decision theoretic formulation of planning is computationally impractical. Techniques need to be developed to do efficient decision theoretic planning. Work in AI plan generation has exploited information contained the structure of qualitative representations to guide efficient plan construction. These techniques should be applied to decision theoretic representations as well. Second, AI has developed many representations that allow useful structuring of knowledge about the world. Decision Theory has concentrated on representing beliefs and desires. Integration of AI and decision theoretic representations would yield powerful representation languages. Some of the benefits of such work can already be seen in the research combining temporal and decision theoretic representations.

Construction Engineering and Management is a broad area of civil engineering practice which includes: (1) planning and engineering of the temporary facilities for construction sites; (2) management of the construction process; and (3) rehabilitation, repair and maintenance of engineered facilities, where the traditionally distinct design and construction roles of participants have become merged. The broad scope of this field, combined with the empirical nature of many facets of construction engineering and management practice, have led to a significant amount of expert system activity in this domain.

It is a common Establishment practice in the UK to ask an expert, recognized for his/her sound views, to give a view on controversial matters of public interest. There are recent examples in such varied fields as accounting standards, export credits, and microgravity. Administrations—for all the current fashion for “built-in evaluation”—have no time or inclination to take a look backwards at these autographs. In this paper the author, who was a civil servant, and has worked for ten years as a consultant, including two years in the European Commission, considers, with the help of hindsight, the Lighthill Report of 1972 on AI.

This review implies making judgements about the progress of AI which cannot help being contentious. First, there will be disagreements about judgements, or even assertions—the author cannot claim authority over the whole field, unless as a voyeur; there may be important developments he has failed to take account of. Second, there will be arguments that Lighthill was to an extent self-fulfilling—that is, that there would have been more advance if some aspects of AI had not been checked at his advice. Third, it will be said that it takes time to work up a new computational concept—or any other new thing.

However, whatever the reservations, the main conclusion put forward is that there was an element of over-optimism about AI in general which Lighthill had reason to castigate. In directing his comments to the utility of AI in specific fields Lighthill got some things wrong, but, on balance—which is not easy to strike—probably more things right than wrong.

Extrapolating to the present day the writer questions whether, in the UK after Alvey, and not least in Japan, resources are not being expended on AI objectives which are intellectually stimulating but remain too far from the application's sharp end; and therefore whether there is still not too much “jam tomorrow” about AI. Whether or not this questioning is judged to be wrongheaded, perhaps it will prove useful to have stirred discussion on the subject.

Decision-making is an integral part of any consideration of the discipline of control Systems. This implies feedback of knowledge in some format, whether it is mediated via human or machineoriented methods. The application of AI methodologies to control Systems engineering is described in this review under two broad categories of design and implementation. The design of control strategies is itself divided into phases of modelling and simulation, identification, and algorithm selection and tuning. The aspects of implementation which are covered include real-time considerations such as knowledge-based control and fuzzy logic, multi-sensor data fusion, fault detection and Human–Computer Interaction (HCI).

The theme of this paper is to review work carried out in the area of applying expert systems and artificial intelligence in electrical power engineering. It surveys expert systems and artificial-intelligence-based algorithms developed for solving decision problems in power network and generator fault diagnosis, reactive power and voltage control, restoration of power supply, determination of load circuits for shedding in under-frequency load shedding schemes, generator scheduling, allocation of loads and circuits, load-flow planning, generation expansion planning and distribution expansion planning.

Frame systems occupy an important place among formalisms for computer-based knowledge representation. A common concern about frame systems, however, is that they are not efficient enough. We argue that this is not necessarily true of all possible systems, and that the trade-off between generality and efficiency has not been fully explored. While many systems provide generality at the expense of performance, systems closer to the low end of the spectrum have not been investigated nearly as much. Those systems are well suited for applications that need flexible knowledge representation but cannot afford the high performance price.

We describe in detail KR, a very efficient frame system that provides mechanisms for knowledge representation including user-defined inheritance and relations, object-oriented programming, and constraint maintenance. The system is simple and compact and does not include some of the more complex functionality, but it is highly optimized and offers excellent performance for a variety of applications.