Showing papers on "Applications of artificial intelligence published in 1984"

STEAMER: An Interactive Inspectable Simulation-Based Training System

Abstract: The Steamer project is a research effort concerned with exploring the use of AI software and hardware technologies in the implementation of intelligent computer-based training systems. While the project addressed a host of research issues ranging from how people understand complex dynamic systems to the use of intelligent graphical interfaces, it is focused around the construction of a system to assist in propulsion engineering instruction. The purpose of this article is to discuss the underlying ideas which motivated us to initiate the Steamer effort, describe the current status of the project, provide a glimpse of our planned directions for the future, and discuss the implications of Steamer for AI applications in other instructional domains.

Artificial intelligence and its applicability to geographical problem solving

Abstract: Artificial Intelligence (AI) may be regarded as an attempt to understand the processes of perception and reasoning that underlie successful problem-solving and to incorporate the results of this research in effective computer programs. At present, AI is largely a collection of sophisticated programming techniques. Many of these techniques are based on the premise that the manner in which knowledge is acquired, organized, accessed and modified in both humans and machines provides the basis of “intelligent” decision-making. The techniques of AI are applicable to a wide variety of geographical problems, including the modeling of individual and aggregate decision-making, and the construction of expert systems and “intelligent” geographical information systems.

Artificial Intelligence in Parallel Working with many processors at once could accelerate computation enormously—and suggest new ways to think about thinking

Abstract: The current rage in the Artificial Intelligence (AI) community is parallelism: the idea is to build machines with many independent processors doing many things at once. The upshot is that about a dozen parallel machines are now under development for AI alone. As might be expected, the approaches are diverse yet there are a number of fundamental issues in common: granularity, topology, control, and algorithms.

Experience with INTELLECT: Artificial Intelligence Technology Transfer

Abstract: AI technology transfer Is the diffusion of AI research techniques into commercial products. I have been involved in this process since 1975, when the Artificial Intelligence Corporation began to develop ROBOT, the prototype of INTELLECT, a commercially viable natural language interface to data base systems which has been on the market since 1981. In this article, I will discuss AI technology transfer with particular reference to my experiences with the commercialization of INTELLECT. I will begin with the historical perspective of where the field of AI came from, where it is now, and where it is going. Next, I will describe my interpretation of the present market structure for AI products and some specific marketing perspectives. I will then briefly describe the product INTELLECT and its capabilities as an example of a state-of-the-art commercial system. Next, I will describe some of the experiences, which I think are typical, that my company has encountered in commercialize their systems.

For artificial intelligence applications.

Abstract: Data models used in database management have not been built with AI applications in mind. The entities and their relationships in an AI environment transcend in complexity the data semantics of most other databases, so that the expressive power of the "usual" data models becomes insufficient. In AI community databases are viewed as a possible application area ("database front ends") but in AI research itself the databases used tend to be ~~ and are not specified in terms of data models and DBMS based on such. The data model suggested below is a step towards bridging the gap between database theory and AI databases. Int roduct ion The complexity of problems encountered by AI researchers designing both the knowledge-based expert systems and theoretical models of cognitive entities is notoriously high. The overall task involves many components, of which the most widely studied are knowledge representation languages, various "inference engines" (models of informal reasoning), automatic deduction, models of planning and cognitive processes, parsers and grammars. Although it is currently recognized that databases are integral parts of practically any AI system, and although a majority of AI systems employs the database concept, it is a fact that most such databases, however ingeniously built, are inherently ~ ~ and, more importantly, do not, as a rule, reflect the developments in the theory of database management. A very good example is the approach described in the influential book by Charniak, McDermott and Riesbeck (1980) • These authors devote much space to describing the AI databases (predicate calculus-based, slot-and-filler ones,

The Industrialization of Artificial Intelligence: From By-Line to Bottom Line

Abstract: Over the past few years, the character of the AI community has changed. AI researchers used to be able to go about their work in peace, while the rest of the world ignored them. As the promise of partical applications of AI has slowly become reality, new players have entered the field, changing its nature forever. The quiet, intellectual community of AI researchers has been augmented by a hoard of other interested parties, including the press, the financial community, and the technology entrepreneurs. Since we cannot go back and hide in the ivory tower, we may as well take the time to explore our new environment. I invite you to join me in a guided tour of the new AI community. Let's begin by analyzing the basic motivation for commercial interest in AI.

Artificial Intelligence Research in Statistics

Abstract: The initial results from a few AI research projects in statistics have been quite interesting to statisticians: Feasibility demonstration systems have been built at Stanford University, AT-T bell Laboratories, and the University of Edinburgh. Several more design studies have been completed. A conference devoted to expert systems in statistics was sponsored by the Royal Statistical Society. On the other hand, statistic as a domain may be of particular interest to AI researchers, for it offers both tasks well suited to current AI capabilities and tasks requiring development of new AI techniques.

Applications Of Artificial Intelligence In Automated Route Planning

Abstract: This paper addresses the problem of route planning for ground vehicles. The problem is decomposed into two principal sub-problems: manipulation of a multi-dimensional knowledge base to result in a "composite map" consistent with the current mission goals, and a subsequent search procedure applied to this composite map to result in high performance routes. The relevance of expert systems and other techniques for route planning is discussed. A particularly efficient search procedure is applied to several example composite maps to demonstrate the power of the approach.

Artificial intelligence: Underlying assumptions and basic objectives

Abstract: Artificial intelligence (AI) research has recently captured media interest and it is fast becoming our newest “hot” technology. AI is an interdisciplinary field which derives from a multiplicity of roots. In this article we present our perspectives on methodological assumptions underlying research efforts in Al. We also discuss the goals (design objectives) of AI across the spectrum of subareas it comprises. We conclude by discussing why there is increased interest in AI and whether current predictions of the future importance of AI are well founded.

Keynote Address-Artificial Intelligence and simulation

Abstract: Artificial Intelligence is the latest buzzword and one of the hottest topics in the scientific community today. Some experts are proclaiming that Artificial Intelligence (AI) has already emerged as one of the most significant technologies of this century. Proponents are declaring that it will completely revolutionize management and the way we use computers. If these claims are even half true, then AI is bound to have a profound effect upon the art and science of simulation. The purpose of this paper is to provide a current overview of this rapidly evolving field, examine the potential of AI in simulation and the inevitability of it. We propose to explore the probable impact as well as forecast the directions it is likely to take.

Actors: the stage is set

Abstract: The software systems being developed by artificial intelligence researchers are no different in many respects from the systems being developed by the business and scientific communities. They are large, intricate systems often very difficult to implement if they must also be understandable, reliable and maintainable. The AI community has been developing their own ideas to deal with the construction of such systems; ideas whose application is not restricted to the AI domain. This paper introduces the concept of actors, discusses their properties, and describes how actor systems and languages are being used in application areas outside AI. 17 references.

Really arguing with your computer in natural language

Abstract: Recently, the computer science community has been hearing a lot about "fifth generation" computers and the Japanese large-scale project to build intelligent software that can "think," "reason," and "understand human languages." It is in the field of artificial intelligence (AI) where such intelligence machines and programs are being designed and created. How far along is the field of AI? How close are AI programs to being able to "reason" or "understand" as humans do? This paper is intended to give scientists outside the field of AI some insight about the problems, issues, and current status of computational models of human argumentation.

Artificial intelligence applications concepts for the remote sensing and earth science community

Abstract: The following potential applications of AI to the study of earth science are described: (1) intelligent data management systems; (2) intelligent processing and understanding of spatial data; and (3) automated systems which perform tasks that currently require large amounts of time by scientists and engineers to complete. An example is provided of how an intelligent information system might operate to support an earth science project.

Artificial Intelligence Applications to Maintenance

Abstract: : The maintenance of modern military systems employs a variety of automation. Built-In-Test provides on-line fault detection and some isolation, Automatic Test Equipment is indispensable at intermediate and depot repair stations, and automated maintenance aids and trainers abound. These developments were designed to speed maintenance and to compensate for declining skill levels in the maintenance force. They are currently far from satisfactory. Modern maintenance is characterized by excessive false alarms and unnecessary removals at all levels of maintenance. The results of these deficiencies are long maintenance times, resources wasted in unnecessary for inefficient maintenance actions, and systems out of action which need not be. Correcting these problems would therefore provide both an economic advantage and a force multiplier. To create quantum improvements in maintenance will require the application of radical changes to the technology. One possibility is the application of Artificial Intelligence (AI) techniques to maintenance. AI is beginning to see application to practical problems in many disciplines, and hence is potentially capable of relatively rapid implementation into military systems. At present, DoD efforts in applying AI to maintenance are small and exploratory. The task of the Artificial Intelligence Applications committee was to examine the opportunities for applying AI to maintenance, assess the costs, risks, and development time required, and provide recommendations to the DoD for action.

We Need Better Standards for Artificial Intelligence Research: President's Message

Abstract: If we had better standards for evaluating research results in AI the field would progress faster.

Artificial Intelligence Applications to Testability.

Abstract: : This study provides the foundation for a logical and cost-effective program for applying Artificial Intelligence to electronic testability for the military. Emphasis is on those artificial intelligence (AI) techniques capable of practical application with low risk development within three to five years. The primary near term applications are design support and maintenance applications. Eight potential applications are developed and evaluated: 1) Computer Aided Preliminary Design for Testability, 2) Smart Built-in Test, 3) Smart System Integrated Test, 4) Box Level Maintenance Expert, 5) System Level Maintenance Expert, 6) Smart Maintenance Expert, 7) Automatic Test Program Generation, and 8) Smart Bench Tester. All of these application opportunities can be implemented with engineering workstations which are becoming available directly to designers. Originator-supplied keywords include: Artificial intelligence; Automated diagnostics; BIT; Design for testability; Fault isolation; Predictive maintenance; Engineering workstations.

Rand's Experience in Applying Artificial Intelligence Techniques to Strategic-Level Military-Political War Gaming,

Abstract: : This paper highlights some recent experience in Rand's Strategy Assessment Center (RSAC), a large-scale DoD program to develop new concepts and techniques combining features of war gaming and analytic modeling. The centerpiece of the program is a system for automated war gaming in which some or all political and military national decisions can be made by automatons, and in which both force operations and combat are described by theater and strategic-level models. The RSAC development program is providing a wealth of technical and managerial lessons in adapting and extending such artificial intelligence (AI) techniques as scripts, production rules, English-readable programming languages, goal-directed search, and pattern matching. Most previous AI applications have dealt with smaller and less-complex problems, and have not had to combine AI techniques with those of well-structured system programming and algorithmic combat modeling. Also, the RSAC integration effort has brought together professionals from at least a half-dozen cultures with good ideas but different notions of what constitutes good practice and natural logic. The experience has been illuminating, and the emerging synthesis is unlike previous simulations of which we are aware. (Author)

Artificial Intelligence In Automatic Target Recognizers: Technology And Timelines

Abstract: The recognition of targets in thermal imagery has been a problem exhaustively analyzed in its current localized dimension. This paper discusses the application of artificial intelligence (AI) technology to automatic target recognition, a concept capable of expanding current ATR efforts into a new globalized dimension. Deficiencies of current automatic target recognition systems are reviewed in terms of system shortcomings. Areas of artificial intelligence which show the most promise in improving ATR performance are analyzed, and a timeline is formed in light of how near (as well as far) term artificial intelligence applications may exist. Current research in the area of high level expert vision systems is reviewed and the possible utilization of artificial intelligence architectures to improve low level image processing functions is also discussed. Additional application areas of relevance to solving the problem of automatic target recognition utilizing both high and low level processing are also explored.

Nothing artificial please

Abstract: The author assesses the work being carried out in the field of artificial intelligence and argues that we are a long way from true machine intelligence. He feels that the aims of AI research are often misunderstood and explains that most of the effort is directed toward extending the capabilities of machines to make them more useful tools, not toward making ersatz human minds.

Autonomous Vehicle Control Using AI (Artificial Intelligence) Techniques

Abstract: : A review of early work on a project to develop autonomous vehicle control technology is presented. The primary goal of this effort is the development of a generic capability that can be specialized to a wide range of DoD applications. The emphasis in this project is development of the fundamental artificial intelligence-based technology required by autonomous systems and the implementation of a testbed environment to evaluate and demonstrate the system capabilities. (Author)

A comparison of LISP and MUMPS as implementation languages for knowledge-based systems.

Abstract: Major components of knowledge-based systems are summarized, along with the programming language features generally useful in their implementation. LISP and MUMPS are briefly described and compared as vehicles for building knowledge-based systems. The paper concludes with suggestions for extensions to MUMPS that might increase its usefulness in artificial intelligence applications without affecting the essential nature of the language.

Towards a data model for artificial intelligence applications

Abstract: Data models used in database management have not been built with AI applications in mind The entities and their relationships in an AI environment transcend in complexity the data semantics of most other databases, so that the expressive power of the "usual" data models becomes insufficient In AI community databases are viewed as a possible application area ("database front ends") but in AI research itself the databases used tend to be ad hoc and are not specified in terms of data models and DBMS based on such The data model suggested below is a step towards bridging the gap between database theory and AI databases