Showing papers on "Applications of artificial intelligence published in 1988"

Future intelligent information systems: AI and database technologies working together

Abstract: The effective application of AI Technology and the development of future computing systems require the integration of AI and Database Technologies The integration will benefit both AI and Databases and will substantially advance the state of computing

VLSI and rule-based systems

Abstract: Many AI applications today employ some form of if-then rule-based programming. In languages stressing production rules, such as OPS, data is pattern matched against the if part until all literals are satisfied; the then part then indicates how the database should be changed. In more deductive languages like Prolog, the match is between a goal literal whose truth is unknown, and the then part of a rule. A successful match causes a series of goals from the if part to be solved in a similar manner.

What every engineer should know about artificial intelligence

Abstract: From the Publisher: Engineers can profit from the revolution in AI research that is changing the ground rules of the profession. AI expert and consultant William Taylor provides a practical explanation of the parts of AI research that are ready for use by anyone with an engineering degree and that can help engineers do their jobs better. Taylor tours the field of artificial intelligence in a highly readable and engaging manner, outlining in detail how engineers can work with AI. In separate chapters he discusses the three basic programming styles - function-based programming, object-oriented programming, and rulebased programming - as well as the use of Lisp and Prolog. He concludes by offering several suggestions for getting started in the field. As Taylor defines it, AI is a programming style that has much in common with engineering practice: programs operate on data according to rules in order to accomplish goals. While the term "artificial intelligence" is generally defined as meaning the design of computers to think the way people do, Taylor points out that for engineering purposes it is more accurately defined as a few software ideas that work well enough to be used. And as AI technology matures, computers will be able to provide actual design help. They will, in effect, serve as design apprentices, offering suggestions and handling actual parts of the design. William A. Taylor is an international consultant on the practical applications of artificial intelligence and has spent several years giving seminars on AI to senior engineers and engineering management.

Why Engineers Should Not Use Artificial Intelligence

Abstract: It can be said that the most promising field within computer science is Artificial Intelligence, often simply known as AI. Some will interpret this as meaning that AI is a field that holds great pr...

A software and hardware environment for developing AI applications on parallel processors

Abstract: This paper describes and reports on the use of an environment, called Agora, that supports the construction of large, computationally expensive and loosely-structured systems, e.g. knowledge-based systems for speech and vision understanding. Agora can be customized to support the programming model that is more suitable for a given application. Agora has been designed explicitly to support multiple languages and highly parallel computations. Systems built with Agora can be executed on a number of general purpose and custom multiprocessor architectures.

Artificial Intelligence and the Management Science Practitioner: Rational Choice and Artificial Intelligence

Abstract: Current research in AI, while focusing on the practical problems associated with knowledge engineering and design issues, has not addressed a key question: What is a theoretical basis for AI? This paper addresses the issue of defining a theoretical basis for AI and thus illustrating the common bonds between AI, computer science, economics, psychology, management science, and operations research (MS/OR).

A connectionist model for material handling

Abstract: Connectionist models have traditionally been motivated by the desire to imitate human intelligence by copying biological information processing mechanisms. We can also apply them to tasks that are not usually associated with human cognition, by taking advantage of promising mappings between their features (such as distribution, local computation, constraint propagation and computation by relaxation) and certain problem domains. This paper reports the design and implementation of CASCADE, a system for performing the material handling function in a discrete parts manufacturing environment. CASCADE draws heavily on connectionist models, and has been implemented in an experimental machining cell (Parunak e al., Fractal actors for distributed manufacturing control. Proceedings of the 2nd IEEE Conference on AI Applications, 1985, Parunak et al., An architecture for heuristic factory control. Proceedings of the 1986 American Control Conference, 1986). We discuss • • the problem domain of material handling; • • the connectionist framework that we are using; • • the structure of CASCADE in terms of the connectionist model; and • • some computational implications of the model that we exhibit.

The application of AI to law

Abstract: There is much interest in moving AI out into real world applications, a move which has been encouraged by recent funding which has attempted to show industry and commerce can benefit from the Fifth Generation of computing. In this article I suggest that the legal application area is one which is very much more complex than it might — at first sight — seem. I use arguments from the sociology of law to indicate that the viewing of the legal system as simply a rule-bound discipline is inherently nave. This, while not new in jurisprudence, is — as the literature of AI and law indicates — certainly novel to the field of artificial intelligence. The socio-legal argument provided is set within the context of AI as one more example of the failure of scientific success and method to easily transmit itself over into the social sciences.

Big problems for AI

Abstract: I compare the big problems studied in artificial intelligence and related fields in order to understand some major changes -- both internal and external -- recently suffered by AI. The comparison finds AI with few problems to call its own, and I identify some further major changes that may occur soon.

Artifcial intelligence and software engineering: a survey of possibilities

Abstract: This paper is a broad-based review of the area of overlap between the fields of artificial intelligence (AI) and software engineering (SE). A taxonomy of this overlap area is developed and related to other major attempts to address the interaction between these two fields. Each of the three major subareas — AI-based support environments; AI mechanisms and techniques in practical software; and software engineering tools and techniques in AI software — is described and illustrated with representative examples. Finally, it is noted that the area of overlap is changing and thus any current attempt to map out the possibilities is to be viewed as speculation.

A Voice-activated Robot With Artificial Intelligence

Abstract: A voice-activated robot arm with intelligence is presented. The proposed system consists of an IBM PC/AT microcomputer, MICROEAR voice activated hardware, and a Scorbot ER-III robot with a controller. The proposed robot is capable of understanding the meaning of natural language commands. After interpreting the voice commands a series of control data for performing a task are generated. Finally, the robot actually performs the task. Artificial intelligence techniques were used to make the robot understand voice commands and act in the desired mode. >

Mapping the integration of artificial intelligence into telecommunications

Abstract: The relationship between two rapidly evolving technologies, artificial intelligence (AI) and telecommunications, is examined. The current use of AI is primarily as expert systems, designed to aid in diagnosing complex equipment in an offline mode. Factors that led to deployment of expert systems in telecommunications domains are examined, and the possible future evolution of both technologies is charted. It is found that both technologies share common drivers, which contribute to the mapping between future telecommunications trends and AI capabilities. This leads to an exploration of how specific applications, such as natural language processing, distributed AI, and speech recognition, may be used with expert systems in the future intelligent network. >

Grammatical Analysis of English by Statistical Pattern Recognition

Abstract: Artificial Intelligence and Computational Linguistics researchers are currently debating the value of ‘Deep’ knowledge-representations in language processing and related computations. Incorporating deep knowledge as well as surface statistical pattern recognition requires much greater processing, but it has been assumed that, for many applications of Artificial Intelligence, purely surface statistical analyses cannot yield useful results. One NLP application provides a counter-argument to this widespread tenet: a system for grammatical error detection, using only probabilistic, Markovian, pattern-matching was devised, and in tests compared favourably with a much larger system which computed deep grammatical analyses of each sentence. Those who argue that statistical pattern recognition has no place in Computational Linguistics or Artificial Intelligence have still to prove their case.

PC Software for Artificial Intelligence Applications.

Abstract: Our review has emphasized that AI tools are programming languages inspired by some problem-solving paradigm. We want to underscore their status as programming languages; even if an AI tool seems to fit a problem perfectly, its proficient use still requires the training and practice associated with any programming language. The programming manuals for PC-Plus, Smalltalk/ V, and Nexpert Object are all tutorial in nature, and the corresponding software packages come with sample applications. We find the manuals to be uniformly good introductions that try to anticipate the problems of a user who is new to the technology. All three vendors offer free technical support by telephone to licensed users. AI tools are sometimes oversold as a way to make programming easy or to avoid it altogether. The truth is that AI tools demand programming—but programming that allows you to concentrate on the essentials of the problem. If we had to implement a diagnostic system, we would look first to a product such as PC-Plus rather than BASIC or C, because PC-Plus is designed specifically for such a problem, whereas these conventional languages are not. If we had to implement a system that required graphical interfaces and could benefit from inheritance, we would look first to an object-oriented system such as Smalltalk/V that provides built-in mechanisms for both. If we had to implement an expert system that called for some mix of AI and conventional techniques, we would look first to a product such as Nexpert Object that integrates various problem-solving technologies. Finally, we might use FORTRAN if we were concerned primarily with programming a well-defined numerical algorithm. AI tools are a valuable complement to traditional languages.

Artificial Intelligence Applications in Engineering

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Artificial Intelligence Applications for Operation and Maintenance: Japanese Industry Cases

Abstract: A nuclear power plant as a typical man-machine system of the modern industry needs an efficient human window through which operators can observe every necessary details of the plant for its safe and reliable operation. Much efforts have been devoted to the development of the computerized operator support systems (COSS).

Practical applications of artificial intelligence in naval engineering

Abstract: This paper examines the historical oscillations of artificial intelligence (AI). It traces the very beginnings from 1956 at the Dartmouth Summer Research Project on Artificial Intelligence, through the depressions of the 1960s and 1970s, to the exponentially increasing number of successful applications in the mid-1980s. It shows how to select problems which can be solved with the aid of expert systems. The paper also identifies areas such as common sense reasoning, automated machine learning, and complex design synthesis which are now beyond the state of the art and will be for years to come. Artificial intelligence programs are able to diagnose faults and classify solutions in narrowly defined specialties even when the data is “fuzzy,” but they have not exhibited autonomous “thinking.” Just as conventional computer programming has alleviated the burden of calculating, AI expert systems will streamline the processing of logical data. Both of these computer techniques are cost effective when they are applied to well defined tasks, since computers are faster than people and error free for routine tasks.

What AI Can Do for Battle Management: A Report of the First AAAI Workshop on AI Applications to Battle Management

Abstract: The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. The workshop organizer, Pete Bonasso, sent a point paper to a number of invited presenters giving his opinion of what AI could and could not do for battle management. This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time. Finally, there are several areas -- cooperating systems and terrain reasoning, for example -- where, given some impetus, AI might be able to provide help in the near future.

Neural networks with asynchronous control

Abstract: Neural network studies have previously focused on monolithic structures. The brain has a bicameral nature, however, and so it is natural to expect that bicameral structures will perform better. This dissertation offers an approach to the development of such bicameral structures. The companion neural structure takes advantage of the global and subset characteristics of the stored memories. Specifically we propose the use of an asynchronous controller C that implies the following update of a probe vector x by the connection matrix T: $x\sp\prime$ = sgn (C(x, TX)). For a VLSI-implemented neural network the controller block can be easily placed in the feedback loop. In a network running asynchronously, the updating of the probe generally offers a choice among several components. If the right components are not updated the network may converge to an incorrect stable point. The proposed asynchronous controller together with the basic neural net forms a bicameral network that can be programmed in various ways to exploit global and local characteristics of stored memory. Several methods to do this are proposed. In one of the methods the update choices are based on bit frequencies. In another method handles are appended to the memories to improve retrieval. The new methods have been analyzed and their performance studies it is shown that there is a marked improvement in performance. This is illustrated by means of simulations. The use of an asynchronous controller allows the implementation of conditional rules that occur frequently in AI applications. It is shown that a neural network that uses conditional rules can solve problems in natural language understanding. The introduction of the asynchronous controller may be viewed as a first step in the development of truly bicameral structures that may be seen as the next generation of neural computers.

AI tools and techniques for manufacturing systems

Abstract: Artificial intelligence is a discipline engaged in the development and application of methods, tools and techniques for solving logically complex problems. These tools and techniques are now becoming accepted in computer applications in general and their use is proliferating rapidly in areas other than computer science. This paper reviews part of the existing artificial intelligence technology with a special view toward potential applications in manufacturing systems. The review includes automatic planning, learning, programming by examples, qualitative modelling and AI programming languages.

Similarity-based reasoning about diagnosis of analog circuits

Abstract: This paper advocates the use of similarity-based reasoning to improve the efficacy of AI applications dealing with diagnosis of electronic circuits. A paradigm for similarity-based matching is presented, application of this paradigm to diagnosis of electronic circuits is discussed, and a system architecture that supports this concept is described.

The 1988 Goddard Conference on Space Applications of Artificial Intelligence

Abstract: This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies.

The Impact of Artificial Intelligence on Society

Abstract: This paper presents an introduction to a number of social issues which may arise as a result of the diffusion of Artificial Intelligence (AI) applications from the laboratory to the workplace and marketplace. Four such applications are chosen for discussion: expert systems, image processing, robotics, and natural language understanding. These are briefly characterized and possible areas of misuse are explored. Of the many social issues of concern, four are selected for treatment here as representative of other potential problems likely to follow such a powerful technology as AI. These four are work (how much and of what kind), privacy (on which the assault continues), decision-making (by whom and for whose benefit), and social organization (how in a society in which intelligent systems perform so many functions). Finally it is argued that both a major programme of study in this field be launched and that practitioners assume the responsibility to inform the public about their work.

Applications of artificial intelligence to digital photogrammetry

Abstract: A great deal of interest has arisen lately in the application of expert systems to problems in which computer solutions were previously inapplicable. The aim of this research was to explore the application of expert systems to digital photogrammetry, specifically to photogrammetric triangulation, feature extraction, and photogrammetric problem solving. In 1987, prototype expert systems were developed for doing system startup, interior orientation, and relative orientation in the mensuration stage. The system explored means of performing diagnostics during the process. In the area of feature extraction, the relationship of metric uncertainty to symbolic uncertainty was the topic of research. Error propagation through the Dempster-Shafer formalism for representing evidence was performed in order to find the variance in the calculated belief values due to errors in measurements made to gather the initial evidence needed to begin labeling of observed image features with features in an object model. In Photogrammetric problem solving, an expert system is under continuous development which seeks to solve photogrammetric problems using mathematical reasoning. The key to the approach used is the representation of knowledge directly in the form of equations, rather than in the form of if-then rules. Then each variable in the equations is treated as a goal to be solved. Once the solution set of equations able to solve the problem is determined, the set is submitted to the Vaxima (MACSYMA) expert system for solution. Development of a rule-based part to deal with the heuristic knowledge needed in solving the equations, such as choosing optimum equations, and selecting suitable approximations, has been described.

Experiments with microcomputer-based artificial intelligence environments

Abstract: The U.S. Geological Survey (USGS) has been experimenting with the use of relatively inexpensive microcomputers as artificial intelligence (AI) development environments. Several AI languages are available that perform fairly well on desk-top personal computers, as are low-to-medium cost expert system packages. Although performance of these systems is respectable, their speed and capacity limitations are questionable for serious earth science applications foreseen by the USGS. The most capable artificial intelligence applications currently are concentrated on what is known as the “artificial intelligence computer,” and include Xerox D-series, Tektronix 4400 series, Symbolics 3600, VAX, LMI, and Texas Instruments Explorer. The artificial intelligence computer runs expert system shells and Lisp, Prolog, and Smalltalk programming languages. However, these AI environments are expensive. Recently, inexpensive 32-bit hardware has become available for the IBM/AT microcomputer. USGS has acquired and recently completed Beta-testing of the Gold Hill Systems 80386 Hummingboard, which runs Common Lisp on an IBM/AT microcomputer. Hummingboard appears to have the potential to overcome many of the speed/capacity limitations observed with AI-applications on standard personal computers. USGS is a Beta-test site for the Gold Hill Systems GoldWorks expert system. GoldWorks combines some high-end expert system shell capabilities in a medium-cost package. This shell is developed in Common Lisp, runs on the 80386 Hummingboard, and provides some expert system features formerly available only on AI-computers including frame and rule-based reasoning, on-line tutorial, multiple inheritance, and object-programming.