Showing papers in "Ai Magazine in 1986"
TL;DR: In this paper, the Dempster-Shafer theory is viewed in the context of relational databases as the application of familiar retrieval techniques to second-order relations in first normal form.
Abstract: During the past two years, the Dempster-Shafer theory of evidence has attracted considerable attention within the AI community as a promising method of dealing with uncertainty in expert systems. As presented in the literature, the theory is hard to master. In a simple approach that is outlined in this paper, the Dempster-Shafer theory is viewed in the context of relational databases as the application of familiar retrieval techniques to second-order relations in first normal form. The relational viewpoint clarifies some of the controversial issues in the Dempster-Shafer theory and facilities its use in AI-oriented applications.
824 citations
TL;DR: Object-Oriented programming has a long history in simulation programs, systems programming, graphics, and AI programming as discussed by the authors, including message passing as in ACTORS and multiple inheritance as in FLAVORS.
Abstract: Many of the ideas behind object-oriented programming have roots going back to SIMULA. The first substantial interactive, display-based implementation was the SMALLTALK language. The object-oriented style has often been advocated for simulation programs, systems programming, graphics, and AI programming. The history of ideas has some additional threads including work on message passing as in ACTORS, and multiple inheritance as in FLAVORS. It is also related to a line of work in AI on the theory of frames and their implementation in knowledge representation languages such as KRL, KEE, FRL, and UNITS.
804 citations
TL;DR: The first blackboard system was the HEARSAY-II speech understanding system (Erman et al.,1980) that evolved between 1971 and 1976 as mentioned in this paper, and many systems have been built that have similar system organization and run-time behavior.
Abstract: The first blackboard system was the HEARSAY-II speech understanding system (Erman et al.,1980) that evolved between 1971 and 1976. Subsequently, many systems have been built that have similar system organization and run-time behavior. The objectives of this article are (1) to define what is meant by "blackboard systems" and (2) to show the richness and diversity of blackboard system designs. The article begins with a discussion of the underlying concept behind all blackboard systems, the blackboard model of problem solving. In order to bridge the gap between a model and working systems, the blackboard framework, an extension of the basic blackboard model is introduced, including a detailed description of the model's components and their behavior. A model does not come into existence on its own, and is usually an abstraction of many examples. In Section 2 the history of ideas is traced, and the designs of some application systems that helped shape the blackboard model are detailed. Part 2 of this article which will appear in the next issue of AI Magazine, describes and contrasts some blackboard systems and discusses the characteristics of application problems suitable for the blackboard method of problem solving.
573 citations
TL;DR: The major limitations in building large software have always been its brittleness when confronted by problems that were not foreseen by its builders, and its bottlenecks can be widened.
Abstract: The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.
361 citations
TL;DR: Examining the joint cognitive system implicit in a hypothetical computer consultant that outputs some form of problem solution reveals some of the problems can occur in cognitive system design-e.g., machine control of the interaction, the danger of a responsibility-authority double-bind, and the potentially difficult and unsupported task of filtering poor machine solutions.
Abstract: This article explores the implications of one type of cognitive technology, techniques and concepts to develop joint human-machine cognitive systems, for the application of computational technology by examining the joint cognitive system implicit in a hypothetical computer consultant that outputs some form of problem solution. This analysis reveals some of the problems can occur in cognitive system design-e.g., machine control of the interaction, the danger of a responsibility-authority double-bind, and the potentially difficult and unsupported task of filtering poor machine solutions. The result is a challenge for applied cognitive psychology to provide models, data, and techniques to help designers build an effective combination between the human and machine elements of a joint cognitive system.
202 citations
TL;DR: Research is described aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision, and two Bnowledge-based factory scheduling systems that implement aspects of this approach are described.
Abstract: To be useful in practice, a factory production schedule must reflect the influence of a large and conflicting set of requirements, objectives and preferences. Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. A variety of constraint-directed inference techniques are defined with respect to this model to provide a basis for intelligently compromising among conflicting concerns. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.
196 citations
193 citations
PARC1
TL;DR: The most widely understood goal of artificial intelligence is to understand and build autonomous, intelligent, thinking machines.
Abstract: The most widely understood goal of artificial intelligence is to understand and build autonomous, intelligent, thinking machines. A perhaps larger opportunity and complementary goal is to understand and build an interactive knowledge medium.
178 citations
TL;DR: The research leading from the GUIDON rule-based tutoring system is reviewed, including the reconfiguration of MYCIN into NEOMYCIN and NEomYCIN's generalization in the heuristic classification shell, HERACLES.
Abstract: I review the research leading from the GUIDON rule-based tutoring system, including the reconfiguration of MYCIN into NEOMYCIN and NEOMYCIN's generalization in the heuristic classification shell, HERACLES. The presentation is organized chronologically around pictures and dialogues that represent conceptual turning points and crystallize the basic ideas. My purpose is to collect the important results in one place, so they can be easily grasped. In the conclusion, I make some observations about our research methodology.
174 citations
TL;DR: What is meant by blackboard systems is defined and the richness and diversity of blackboard system designs are shown to show to bridge the gap between the model and working systems.
Abstract: The first blackboard system was the Hearsay-II speech-understanding system (Erman et al. 1980), which evolved between 1971 and 1976. Subsequently, many systems have been built that have similar system organization and run-time behavior. The objectives of this document (a part of a retrospective monograph on the AGE Project currently in preparation) are (1) to define what is meant by blackboard systems and (2) to show the richness and diversity of blackboard system designs. In Part 1 we discussed the underlying concept behind all blackboard systems -- the blackboard model of problem solving. In order to bridge the gap between the model and working systems, we introduced and discussed the blackboard framework. We also traced the history of ideas and designs of some application systems that helped shape the blackboard model. In Part 2, we describe and contrast existing blackboard systems. Blackboard systems can generally be divided into two categories: application systems and skeletal systems. In application systems, the blackboard system components are integrated into the domain knowledge required to solve the problem at hand.
171 citations
Journal Article•
TL;DR: In this article, CSRL (Conceptual Structures Representation Language) provides structures for representing classification trees, for navigating within those trees, and for encoding uncertainly judgments about the presence of hypotheses.
Abstract: In this article, we present a programming language for expressing classificatory problem solvers. CSRL (Conceptual Structures Representation Language) provides structures for representing classification trees, for navigating within those trees, and for encoding uncertainly judgments about the presence of hypotheses. We discuss the motivations, theory, and assumptions that underlie CRSL. Also, some expert systems constructed with CSRL are briefly described.
TL;DR: The Callisto project was initiated as a research effort to explore project scheduling, control and configuration problems during the engineering prototype development of large computer systems and to devise intelligent project management tools that facilitate the documentation of project management expertise and its reuse from one project to another.
Abstract: Large engineering projects, such as the engineering development of computers, involve a large number of activities and require cooperation across a number of departments. Due to technological and market uncertainties, these projects involve the management of a large number of changes. The Callisto project was born out of realization that the classical approaches to project management do not provide sufficient functionally to manage large engineering projects. Callisto was initiated as a research effort to explore project scheduling, control and configuration problems during the engineering prototype development of large computer systems and to devise intelligent project management tools that facilitate the documentation of project management expertise and its reuse from one project to another. In the first phase of the project, rule-based prototypes were used to build quick prototypes of project management expertise and the project management knowledge required to support expert project managers. In the second phase, the understanding of point solutions was used to capture the underlying models of project management in distributed project negotiations and comparative analysis. This article provides an overview of the problems, experiments, and the resulting models of project knowledge and constraint-directed negotiation.
Journal Article•
TL;DR: The AI lab at the Courant Institute at New York University (NYU) is pursuing many different areas of artificial intelligence (AI), including natural language processing, vision, common sense reasoning, information structuring, learning, and expert systems.
Abstract: The AI lab at the Courant Institute at New York University (NYU) is pursuing many different areas of artificial intelligence (AI), including natural language processing, vision, common sense reasoning, information structuring, learning, and expert systems. Other groups in the Computer Science Department are studying such AI-related areas as text analysis, parallel Lisp and Prolog, robotics, low-level vision, and evidence theory.
TL;DR: The Parametric Interpretation Expert System is a knowledge system for interpreting the parametric test data collected at the end of complex semiconductor fabrication processes, which reflects the way fabrication engineers reason causally about semiconductor failures.
Abstract: The Parametric Interpretation Expert System (PIES) is a knowledge system for interpreting the parametric test data collected at the end of complex semiconductor fabrication processes. The system transforms hundreds of measurements into a concise statement of all the overall health of the process and the nature and probable cause of any anomalies. A key feature of PIES is the structure of the knowledge base, which reflects the way fabrication engineers reason causally about semiconductor failures. This structure permits fabrication engineers to do their own knowledge engineering, to build the knowledge base, and then to maintain it to reflect process modifications and operating experience. The approach appears applicable to other process control and diagnosis tasks.
TL;DR: The primary goal of this article is to present the AI ideas behind KBEmacs, and the construction of applied AI systems is discussed, in general, using the development of KB emacs as a case history.
Abstract: The Knowledge-Based Editor in Emacs (KBEmacs) is the current demonstration system implemented as part of the Programmer's Apprentice project KBEmacs is capable of acting as a semiexpert assistant to a person who is writing a program, taking over some parts of the programming task The abilities of KBEmacs stem directly from a few key AI ideas However, in many ways KBEmacs does not appear to be an AI system, because its abilities are limited and because (like many applied AI systems) the AI ideas are buried in a large volume of code that has little relevance to AI The primary goal of this article is to present the AI ideas behind KBEmacs In addition, the construction of applied AI systems is discussed, in general, using the development of KBEmacs as a case history
TL;DR: The corporate assessment problem is introduced, the limitations of current expert system approaches to the solution to this problem are pointed out, and a more fundamental approach based on recent work in qualitative physics might be fruitful.
Abstract: Historically, the evolution of expert systems has been driven by scientifically based fields such as medicine, geology, and computer engineering. More recently, expert system developers have turned their attention to the highly judgmental decision tasks found in business and finance. We introduce the corporate assessment problem, point out the limitations of current expert system approaches to the solution to this problem, and suggest that a more fundamental approach based on recent work in qualitative physics might be fruitful.
TL;DR: The organization and principles underlying this system are presented and the ongoing research directions are offered to develop an extensive system that ultimately can be used by the marketing organization.
Abstract: This article describes an effort to develop a knowledge-based financial marketing consultant system. Financial marketing is an excellent vehicle for both research and application in artificial intelligence (AI). This domain differs from the great majority of previous expert system domains in that there are no well-defined answers (in traditional sense); the goal here is to obtain satisfactory arguments to support the conclusions made. A large OPS5-based system was implemented as an initial prototype. We present the organization and principles underlying this system and offer our ongoing research directions. The experience gained in the initial prototyping effort is currently being used to further expert systems research and to develop an extensive system that ultimately can be used by the marketing organization.
TL;DR: The expert executive for preliminary design is described, which was developed at The Boeing Company to expedite the design analysis of aerospace vehicles.
Abstract: In the aerospace industry, knowledge is frequently encoded in various procedural programming languages. These programs typically perform computational functions such as simulation modeling; dynamic analyzing; and optimizing in support of the preliminary design, the detailed design, and the test. Design analysis of the product requires that these computer programs be integrated in a specific sequence in terms of their input and output data and order of execution. Because of the complexity of the interrelationships among the programs, numerous delays and errors occur during their integration. These delays and errors can increase costs, cause scheduling crises, and reduce design quality. However, the problem-solving knowledge required to perform the integration function can be formalized in an expert system that “understands” the objectives of the analyst and executes all programs necessary to produce the desired design analysis. This article describes the expert executive for preliminary design, which was developed at The Boeing Company to expedite the design analysis of aerospace vehicles.
TL;DR: The facilities design expert systems (FADES) developed by the author is presented and described to illustrate issues in factory design and an artificial intelligence approach to this problem.
Abstract: This article provides a discussion of factory design and an artificial intelligence (AI) approach to this problem. Major issues covered include knowledge acquisition and representation, design methodology, system architecture, and communication. The facilities design expert systems (FADES developed by the author is presented and described to illustrate issues in factory design.
TL;DR: It is suggested that much a priori ethical thinking is necessary and that, that such a project cannot only stimulate the authors' moral imaginations, but can also tell us much about their moral thinking and pedagogy, whether or not it is ever accomplished in fact.
Abstract: The possibility of constructing a personal AI raises many ethical and religious questions that have been dealt with seriously only by imaginative works of fiction; they have largely been ignored by technical experts and by philosophical and theological ethicists. Arguing that a personal AI is possible in principle, and that its accomplishments could be adjudicated by the Turing Test, the article suggests some of the moral issues involved in AI experimentation by comparing them to issues in medical experimentation. Finally, the article asks questions about the capacities and possibilities of such an artifact for making moral decisions. It is suggested that much a priori ethical thinking is necessary and that, that such a project cannot only stimulate our moral imaginations, but can also tell us much about our moral thinking and pedagogy, whether or not it is ever accomplished in fact.
TL;DR: The approaches to the problem that occurred during the development, integration, and rehosting of OPGEN are described and some methodological guidelines to expert system builders who are concerned with the final delivery of an expert system are provided.
Abstract: The operations sheets generator (OPGEN) is an expert system that helps industrial engineers at the Hazeltine manufacturing and operations facilities plan the assembly of printed circuit boards. In this article, we describe the evolution of OPGEN from its initial development in the Hazeltine research laboratories to its routine use in an integrated manufacturing environment. We describe our approaches to the problem that occurred during the development , integration, and rehosting of OPGEN and provide some methodological guidelines to expert system builders who are concerned with the final delivery of an expert system.
TL;DR: Twenty-five years ago I had a dream, a daydream, if you will, filled with the wild excitement of seeing a machine act like a human being, at least in many ways.
Abstract: Twenty-five years ago I had a dream, a daydream, if you will. A dream shared with many of you. I dreamed of a special kind of computer, which had eyes and ears and arms and legs, in addition to its "brain." I did not dream that this new computer friend would be a means of making money for me or my employer or a help for my country - though I loved my country then and still do, and I have no objection to making money. I did not even dream of such a worthy cause as helping the poor and handicapped of the world using this marvelous new machine. No, my dream was filled with the wild excitement of seeing a machine act like a human being, at least in many ways.
TL;DR: CML is being used in the factory environment to make turbine blade performs and has proven to greatly simplify the task of building complex control systems.
Abstract: A new computer language for manufacturing is being used to link complex systems of equipment whose components are supplied by multiple vendors. The Cell Management Language (CML) combines computational tools from rule-based data systems, object-oriented languages, and new tools that facilitate language processing. These language tools, combined with rule processing, make it convenient to build new interpreters for interfacing and understanding a range of computer and natural languages ; hence, CML is being used primarily to define other languages in an interpretive environment, that is, as a meta-interpreter. For example, in CML it is quite easy to build an interpreter for machine tool languages that can understand and generate new part programs. Once interpreters for different machine and human languages have been constructed, they can be linked together into a system of interpreters. These interpreters can be used to make intelligent decisions for systemwide action planning and diagnostic error recovery. CML is being used in the factory environment to make turbine blade performs and has proven to greatly simplify the task of building complex control systems.
TL;DR: The major accomplishments in AI at Rand are traced with particular emphasis on Rand's research during the past decade, including recent work in expert systems and knowledge-based simulation.
Abstract: This article presents a brief history of artificial intelligence research at the Rand Corporation. Rand has long been a leader in the field of AI, beginning with the seminal work of Newell, Shaw , and Simon some thirty years ago, and continues with recent work in expert systems and knowledge-based simulation. This article traces the major accomplishments in AI at Rand with particular emphasis on Rand's research during the past decade. The references highlight the major Rand documents on AI and related subjects.
TL;DR: The artificial intelligence (AI) diagnostic program has been diagnosing seven large utility generators since July 1984 and has correctly diagnosed a significant number of generator and instrumentation problems.
Abstract: The development of an online turbine generator diagnostic system is described from conception to initial field verification. The system is composed of a data center located in the power plant that collects data from online measurement devices and communicates these data to a centralized diagnostic facility in Orlando, Florida, where the actual diagnosis is done. The resulting diagnosis and recommended actions are transmitted to the power plant where they are displayed to the operator by the data center. The market-place need, initial approaches to the product, system field verification are described. The artificial intelligence (AI) diagnostic program has been diagnosing seven large utility generators since July 1984 and has correctly diagnosed a significant number of generator and instrumentation problems. Issues such as a centralized approach, rule base quality control, and the range of resources needed for a successful product are discussed.
TL;DR: The consensus of government, academic, and industry leaders widely supports the strategic positioning of U.S. and Japanese research and development in mutually beneficial, two-way flows of innovation as discussed by the authors.
Abstract: The consensus of government, academic, and industry leaders widely supports the strategic positioning of U.S. and Japanese research and development in mutually beneficial, two-way flows of innovation. This report is derived from the IJCAI panel titled U.S and Japanese Cooperation in AI and R&D Opportunities, held August 23, 1986 at the University of California at Los Angeles. This panel discussed the sensitive topic of alternatives to nationalistic competitive strategies that have contributed to an extreme trade deficit surpassing $40 billion in 1986. The ideas offered by the panelists shed light on ways our countries' respective scientific communities can blend talents to achieve the best results in reducing trade frictions. Each country has designated AI research as a key to unlock years of generations of technology and has directed billions of dollars to fund this development. The most recognized projects are the U.S. Microelectronics Technology Computer Consortium (MCC) and Japan's Fifth Generation Computer Project (ICOT). Although noting the obstacles, the panelists encouraged specific, shared efforts to ensure the development of a closer working relationship to explore AI's benefits.