Showing papers on "Applications of artificial intelligence published in 1986"

Applications of Artificial Intelligence to Engineering Problems

Abstract: The conference covered general sessions on AI techniques suitable for engineering applications, e.g. knowledge representation, natural language, probability, design methodologies and constraints. This was followed by sessions covering application in mechanical engineering, civil engineering, electrical engineering, and general engineering. Further sessions covered robotics and tools and techniques for building knowledge based systems.

Applications of artificial intelligence in engineering problems

Abstract: This book presents the papers given at a conference on the use of artificial intelligence in engineering. Topics considered at the conference included Prolog logic, expert systems, knowledge representation and acquisition, knowledge bases, machine learning, robotics, least-square algorithms, vision systems for robots, natural language, probability, mechanical engineering, civil engineering, and electrical engineering.

Applications of artificial intelligence techniques to remote sensing

Abstract: Remote sensing is a geographic analysis tool capable of producing large quantities of data in the spectral, temporal and spatial domains Techniques for automating the image analysis process would be advanced by the inclusion of artificial intelligence techniques in the design of image processing systems The remote sensing application which show promise for successful implementation of artificial intelligence techniques are intelligent onboard processing, advanced database interrogation, and the automated analysis of multispectral imagery

Representation and use of metaknowledge

Abstract: The need for expressing and using metalevel knowledge is emerging in the design of several kinds of AI systems. The careful distinction between object-level and metalevel notions and the formalization of the latter has first been carried out by logicians for foundational reasons; subsequently, the distinction has been exploited in Artificial Intelligence and Computation Theory, revealing itself to be of great relevance to Automated Deduction and Problem Solving. This paper concentrates on the use of metaknowledge in building knowledge-based systems. In order to introduce the issue, some motivating examples are presented. We then review various paradigms for combining knowledge and metaknowledge, with the aim of abstracting general criteria that should underly the construction of viable AI systems, as far as metaknowledge is concerned. Furthermore, a general overview of the uses of metaknowledge in AI is provided and, among them, we concentrate on inference control, which can be conveniently exercised by formalizing control strategies at the metalevel and by letting the inference engine depend on metalevel descriptions. The technique is presented with the aid of some examples, chosen from practical AI applications, that are expressed in the formalism of Horn clause logic. The issue of self-descriptive systems is then addressed. A system that embodies and can use an adequate description of itself allows for self-evaluation (e.g., the estimate of the resources needed to perform a given task) and for self-modification (e.g., the automatic improvement of deduction performance by profiting from experience gained in previous deductions).

Artificial intelligence applications in chemistry

Abstract: This book presents the papers given at a conference on the use of expert systems and artificial intelligence in the field of chemistry. Topics considered include a knowledge-engineering facility for building scientific expert systems, a chemistry diagnostic system for steam power plants, computer algebra, handling molecular structures, organic synthesis, and analytical chemistry.

Introduction to artificial intelligence

Abstract: This paper presents an introductory view of Artificial Intelligence (AI). In addition to defining AI, it discusses the foundations on which it rests, research in the field, and current and potential applications.

Survey on special purpose computer architectures for AI

Abstract: In this survey, we will provide a short survey and classification on the current work in special purpose architectures to support AI applications In spite of the growing importance of AI applications, work in the area of designing AI architectures are so diversified that articles were published in other areas besides Al, ranging from psychology, medicine, manufacturing, computer architecture, software engineering, and database management to industrial engineering, operations research, and the list grows The literature search is also complicated by the fact that with the development of the Fifth-Generation Computer Systems, some work in this area is very recent and was published in many foreign countries During our literature search to compile this survey, we systematically went through over sixty different journals published in various countries and proceedings from over fifty conferences in the last twenty years and over seventy books

STAR - A computer language for hybrid AI applications

Abstract: Constructing Artificial Intelligence application systems which rely on both symbolic and non-symbolic processing places heavy demands on the communication of data between dissimilar languages. This paper describes STAR (Simple Tool for Automated Reasoning), a computer language for the development of AI application systems which supports the transfer of data structures between a symbolic level and a non-symbolic level defined in languages such as FORTRAN, C and PASCAL. The organization of STAR is presented, followed by the description of an application involving STAR in the interpretation of airborne imaging spectrometer data.

AI and MAP in the processing industries

Abstract: Artificial intelligence (AI) has the potential to do more for process control in the next 10 years than microprocessors have in the past 10 years. Manufacturing Automation Protocol (MAP) could do for digital systems what 3 to 15 psi did for pneumatic systems and 4 to 20 ma did for analog electronic systems. This article reviews these technologies, gives real examples of their use in the processing industries, and shows how they are related. In the broadest sense, AI is the science of developing computer systems to emulate human intelligence (reasoning, decision making, language, learning, etc.). Although this includes several technologies, the branch of AI currently used in the processing industries is ''expert systems.'' The intent of an expert system is to simulate the decision making an expert performs to solve a problem. The decision-making process is converted to heuristics (rules of thumb) and deep knowledge (process model) that are put into the expert system's data base (knowledge base). The rules of thumb are usually translated into statements of the type ''IF this happens THEN take this action'' (examples to follow). The major advantage is assisting those with only limited knowledge of a subject by giving them access to themore » decision making process used by someone very knowledgeable in the subject.« less

Artificial intelligence and business management

Abstract: After introducing the concept of artificial intelligence (AI), the authors of this text discuss the scope and limitations of AI technology in the various subfields that are expected to be relevant to business management systems - natural language processing, voice processing, image processing, and intelligent robots. The authors survey each subfield in some detail, not because they expect business managers to absorb all the technical details but because a proper appreciation of the potential of AI technology must be founded on the technicalities of the discipline. Therefore, substantial detail and follow-up references are provided for the reader to explore to whatever depth seems most appropriate. The authors also take a close look at expert systems technology as this subfield of AI has a reasonable claim to be the major commercialization of AI technology to date. They discuss the scope and limitations of this particular technology, and present its advantages and disadvantages with respect to applications in business. Having surveyed the technical possibilities, the authors present the resources for AI that are available: technical resources, both software and hardware, and human resources. In the final part of the book they examine the likely impacts of AI technology from the management perspective.

Expert systems: The structure, history, and future of successful AI applications

Abstract: The evolution of expert systems is described. The composition of expert systems is examined, and the manner in which they differ from standard computer programs is highlighted. Existing expert systems are examined to illustrate the properties and capabilities of these programs.

Artificial Intelligence Applications Of Fast Optical Memory Access

Abstract: There is a large potential payoff for successful application of rapid laser beamsteering techniques to optical media access. Optical disc storage provides a huge amount of memory; can this memory be utilized effectively for AI applications? The ultimate capability of an AI expert may be related to the amount of memory available. This paper describes the considerations which are likely to be important for implementation of rapid memory access techniques. Two issues are likely to be important. First, rapid access to the storage medium will have a strong influence on real-time performance, and second, automated learning and organization of knowledge will be important to get large amounts of information into the expert system. A review of laser beamsteering techniques suitable for random access to optical memory media is presented, grouped according to basic principles of operation. Photorefractive beamsteering, which may be particularly useful for optical computing applications, is discussed in detail. In the second part of the paper, methods for creation, organization, and utilization of very large rule bases are considered, and preliminary experiments in this area are presented.

Implementing Dynamic Arrays: A Challenge for High-Performance Machines,

Abstract: There is an increasing need for high-performance AI machines. What is unusual about AI is that its programs are typically dynamic in the way their execution unfolds and in the data structures they use. AI therefore needs machines that are late-binding. Multiprocessors are often held out as the answer to AI's computing requirements. However, most success with multiprocessing has come from exploiting numerical computations' basic data structure - the static array (as in FORTRAN). A static array's structure does not change, so its elements (and the processing on them) may be readily distributed. In AI, the ability to change and manipulate the structure of data is paramount; hence, the pre-eminence of the LISP list. Unfortunately, the traditional pointer-based list has serious drawbacks for distributed processing. The dynamic array is a data structure that allows random access to its elements (like static arrays) yet whose structure - size and dimensions - can be easily changed, i.e., bound and re-bound at run - time. It combines the flexibility that AI requires with the potential for high performance through parallel operation. A machine's implementation of dynamic arrays gives a good insight into its potential usefulness for AI applications. Therefore, the authors outline themore » implementation of dynamic arrays on a machine that we are developing.« less

Computerized Implementation of Biomedical Theory Structures: An Artificial Intelligence Approach

Abstract: In this paper I discuss the nature of a broad class of biomedical theories which I have termed "middle-range theories." I define them and relate the nature of biomedical theorizing to other investigations, such as a recent inquiry by the National Academy of Sciences. I also suggest that some of the knowledge representation tools from artificial intelligence may give us a purchase on this type of biological theorizing, and try to show in a rather preliminary and exploratory manner by using the lac operon model of genetic control, what some of those AI applications, such as frames and semantic nets, might look like in this context. Finally, I suggest some difficulties, such as the non-monotonicity of reasoning, which such tools may generate.

The emergence of artificial intelligence applications in manufacturing system control

Abstract: The Manufacturing Systems cover a technical field where an intelligent combination of human and artificial capabilities can best solve major problems. Men know how to model the structure of those Systems and are able Lo formulate conveniently the problem of controlling them aiming to specified performance characteristics. But the information and data gathered can be handled and processed only by artificial means, primarily computers. The essential issue in this context consists of properly organizing the human and artificial Tasks, developing and implementing them in mutual accordance so as to make the best use of both potentials. The present paper tries to present in logical sequence the major lines and leading criteria of this approach.

Artificial Intelligence for Space Station Automation: Crew Safety, Productivity, Autonomy, Augumented Capability

Abstract: Artificial intelligence (AI) R&D projects for the successful and efficient operation of the Space Station are described The book explores the most advanced AI-based technologies, reviews the results of concept design studies to determine required AI capabilities, details demonstrations that would indicate the existence of these capabilities, and develops an R&D plan leading to such demonstrations Particular attention is given to teleoperation and robotics, sensors, expert systems, computers, planning, and man-machine interface

Maintenance Engineering Management Applications of Artificial Intelligence

Abstract: The author has surveyed for the Engineering Branch of the Royal Air Force the potential applications of Artificial Intelligence techniques to aircraft maintenance management. This paper identifies the characteristics of faults and maintenance work, covers the characteristics of maintenance management, discusses computer-aiding, and summarises the methodology and the results of the aircraft-oriented survey. Six application classes were identified: Intelligent Front Ends, consultative aids to technical manuals, maintenance regulations and “good practices”, diagnostic aids for novel and familiar faults, an equipment assignment aid, and a maintenance work scheduling aid. The results of the survey are generalised to the maintenance of other types of plant and equipment.

An Appraisal Of Artificial Intelligence Techniques For Industrial Vision Systems

Abstract: The role of Artificial Intelligence (AI) in industrial vision systems is examined It is concluded that there are several, quite distinct areas where AI concepts and techniques are likely to be useful The first is that of designing and planning industrial vision systems The most obvious application is probably that of analysing complex scenes, about which little or nothing is known in advance However, the author argues that in a well designed industrial plant there is very little disorder For example, the orientation of piece parts would not be lost by tossing them into a bin The cost of re-orientating them can be quite expensive The well known "bin picking" problem is thus seen as one for which solutions should be sought but instead avoided The third area where AI techniques might be used is in the task of inspecting either complex objects / assemblies, or those which are made in small quantities The paper concludes with a discussion about the requirement for a convenient language for expressing both image processing and AI algorithms The structure of such a language is described