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Stanford University Knowledge Systems Laboratory

Works: 229 works in 267 publications in 1 language and 300 library holdings
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Most widely held works by Stanford University
Artificial intelligence technical reports. catalogue( Book )

3 editions published in 1985 in English and held by 12 WorldCat member libraries worldwide

Decision procedures by Matthew L Ginsberg( Book )

3 editions published in 1985 in English and held by 8 WorldCat member libraries worldwide

Distributed artificial intelligence is the study of how a group of individual intelligent agents can combine to solve a difficult global problem; the usual approach is to split the original problem into simpler ones and to attack each to these independently. This paper discusses in very general terms the problems which arise if the subproblems are not independent, but instead interrelate in some way. We are led to a single assumption, which we call common rationality, that is provably optimal (in a formal sense) and which enables us to characterize precisely the communication needs of the participants in multi-agent interactions. An example of a distributed computation using these ideas is presented
The CAOS system / Eric Schoen by E Schoen( Book )

2 editions published in 1986 in English and held by 8 WorldCat member libraries worldwide

The CAOS system is a framework designed to facilitate the development of highly concurrent real-time signal interpretation applications. It explores the potential of multiprocessor architectures to improve the performance of expert systems in the domain of signal interpretation. CAOS is implemented in LISP on a (simulated) collection of processor-memory sites, linked by a high- speed communications subsystem. The virtual machine on which it depends provides remote evaluation and packet-based message exchange between processes, using virtual circuits known as streams. To this presentation layer, CAOS adds (1) a flexible process scheduler, and (2) an object-centered notion of agents, dynamically-instantiable entities which model interpreted signal features. This report documents the principal ideas, programming model, and implementation of CAOS. A model of real-time signal interpretation, based on replicated abstraction pipelines, is presented. For some applications, this model offers a means by large numbers of processors may be utilized without introducing synchronization-necessitated software bottlenecks. The report concludes with a description of the performance of a large CAOS application over various sizes of multiprocessor configurations. Lessons about problem decomposition grain size, global problem solving control strategy, and appropriate services provided to CAOS by the underlying architecture are discussed. (Author)
Blackboard systems by Penny Nii( Book )

3 editions published in 1986 in English and held by 8 WorldCat member libraries worldwide

The first blackboard system was the HEARSAY-II speech understanding system 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 document 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 of framework, an extension of the basic blackboard model is introduced, including a detailed description of the model's component and their behavior. A model does not come into existence on its own 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. The author then describes and contrasts existing blackboard systems. Blackboard systems are divided into applications and and skeletal systems. In application systems the blackboard system components are integrated with the domain knowledge required to solve the problem at hand. Skeletal systems are devoid of domain knowledge, and, as the name implies, consist of the essential system components from which application systems can be built by the addition of knowledge and the specification of control (i.e. meta-knowledge). In Section 3.6, the author summarizes the features of the application systems and in Section 4 presents the author's perspective on the utility of the blackboard approach to problem solving and knowledge engineering. (Author)
The heuristic refinement method for deriving solution structures of proteins( Book )

2 editions published in 1986 in English and held by 8 WorldCat member libraries worldwide

Expert systems : working systems and the research literature by Bruce G Buchanan( Book )

2 editions published between 1985 and 1986 in English and held by 8 WorldCat member libraries worldwide

Efficient matching algorithms for the SOAR/OPS5 production system by Daniel J Scales( Book )

2 editions published in 1986 in English and held by 7 WorldCat member libraries worldwide

SOAR is an problem-solving and learning program intended to exhibit intelligent behavior. SOAR uses a modified form of the OPS5 production system for storage of and access to long-term knowledge. As with most programs which use production systems, the match phase of SOAR's production system dominates all other SOAR processing. This paper describes the results of an investigation of various ways of speeding up the matching process in SOAR through additions and changes to the OPS5 matching algorithm
Review of Sowa's "Conceptual structures" by William J Clancey( Book )

2 editions published in 1985 in English and held by 7 WorldCat member libraries worldwide

Conceptual Structures is a bold, provocative synthesis of logic, linguistics, and artificial Intelligence research. At the very least Sowa has provided a clean, well-grounded notation for knowledge representation that many researchers will want to emulate and build upon. At its best, Sowa's notation and proofs hint at what a future Principia Mathematica of knowledge and reasoning may look like. No other AI text achieves so much in breadth, style, and mathematical precision. This is a book that everyone in AI and cognitive science should know about, and that experienced researchers will profit from studying in some detail. (Author)
Applying a qualitative modeling shell to process diagnosis : the caster system by Timothy F Thompson( Book )

3 editions published in 1986 in English and held by 7 WorldCat member libraries worldwide

The purpose of knowledge engineering is to develop partial-qualitative models for solving practical problems. These models--called knowledge bases in expert systems--must have appropriate diagnostic knowledge to deal with the real-world problems. In general, solutions to diagnostic problems can be either selected from a set of preenumerated alternatives (for known conditions) or constructed (for novel problems or those that combine multiple, interacting disorders in an unforeseen way). While engineering design is often thought of as a constructive problem-solving process, diagnosis is typically thought of as a selection or classification problem. But the solution method is not inherent in the task itself. Instead, it depends on the problem solver's previous knowledge, requirements for customization, and the like. Nevertheless, useful programs can be developed that solve diagnostic problems by selection alone. We believe that starting with a well-defined classification procedure and a relational language for stating the classification model eases the development of a program that diagnosis by selection. To test this thesis. we built an expert system, called Caster, that addresses a particular diagnostic problem: malfunctions in industrial sandcasting. Our goal was to demonstrate that these control structures, developed for a medical diagnosis problem, are general and applicable to engineering applications
Report by Stanford University( )

in English and held by 6 WorldCat member libraries worldwide

Selection of KSL/HPP research literature appropriate to AI
Some approaches to knowledge acquisition by Bruce G Buchanan( Book )

1 edition published in 1985 in English and held by 5 WorldCat member libraries worldwide

Viewing knowledge bases as qualitative models by William J Clancey( Book )

2 editions published in 1986 in English and held by 5 WorldCat member libraries worldwide

The concept of a qualitative model provides a unifying perspective for understanding how expert systems differ from conventional programs. Knowledge bases contain qualitative models of systems in the world, that is primarily non-numeric descriptions that provide a basis for explaining and predicting behavior and formulating action plans. The prevalent view that a qualitative model must be a simulation, to the exclusion of prototypic and behavioral descriptions, has fragmented our field, so that we have failed to usefully synthesize what we have learned about modeling processes. For example, our ideas about scoring functions and casual network traversal, developed apart from a modeling perspective, have obscured the inherent explanatory nature of diagnosis. While knowledge engineering has greatly benefited from the study of human experts as a means of informing model construction, overemphasis on modeling the expert's knowledge has detracted from the primary objective of modeling a system in the world. Placing artificial intelligence squarely in the evolutionary line of telelogic and topologic modeling, this talk argues that the study of network representations has established a foundation for a science and engineering of qualitative models
Review of Winograd and Flores' understanding of computers and cognition : a favorable interpretation by William J Clancey( Book )

2 editions published in 1986 in English and held by 5 WorldCat member libraries worldwide

Artificial intelligence researchers and cognitive scientists commonly believe that thinking involves manipulating representations. Thinking involves search, inference, and making choices. This is how we model reasoning and what goes on in the brain is similar. Winograd and Flores present a radically different view. They claim that our knowledge is not represented in the brain at all, but rather consists of an unformalized shared background, from which we articulate representations in order to cope with new situations. In contrast, computer programs contain only pre-selected objects and properties, and there is no basis for moving beyond this initial formalization when breakdown occurs. Winograd and Flores provide convincing arguments with examples familiar to most AI researchers. However, they significantly understate the role of representation in mediating intelligent behavior, specifically in the process of reflection, when representations are generated prior to physical action. Furthermore, they do not consider the practical benefits of expert systems and the extent of what can be accomplished. Nevertheless, the book is crisp and stimulating. It should make AI researchers more cautious about what they are doing, more aware of the nature of formalization, and more open to alternative views
From GUIDON to NEOMYCIN and HERACLES in twenty short lessons by William J Clancey( Book )

3 editions published in 1986 in English and held by 4 WorldCat member libraries worldwide

The idea of developing a tutoring program from the MYCIN knowledge base was first described by Ted Shortliffe (1974). In fact it was the mixed-initiative dialogue of the SCHOLAR teaching program (Carbonell, 1970) that inspired Shortliffe to produce the consultation dialogue of MYCIN. He conceived of it as a question-answer program in SCHOLAR's style, using a semantic network of disease knowledge. Shortly after I joined the MYCIN project in early 1975, Bruce Buchanan and I decided that developing a tutoring program would be my thesis project. The GUIDON program was operational in early 1979. This review describes the key ideas in GUIDON and the important developments of the following six years as research continued under funding from the Office of Naval Research (ONR), the Defense Advanced Research Projects Agency (DARPA), and the Army Research Institute. The first three years were covered briefly in an earlier report (Clancey & Buchanan 1982). In general, only publications from this project are cited; many other references appear in the cited publications. Reprints; computer programs
An experiment in knowledge-based signal understanding using parallel architectures by H. D Brown( Book )

2 editions published in 1986 in English and held by 4 WorldCat member libraries worldwide

This report documents an experiment investigating the potential of a parallel computing architecture to enhance the performance of a knowledge-based signal understanding system. The experiment consisted of implementing and evaluating an application encoded in a parallel programming extension of Lisp and executing on a simulated multiprocessor system. The chosen application for the experiment was a knowledge-based system for interpreting pre-processed, passively acquired radar emissions from aircraft. The application was implemented in an experimental concurrent, asynchronous object-oriented framework. This framework, in turn, relied on the services provided by the underlying hardware system. The hardware system for the experiment was a simulation of various sized grids of processors with inter-processor communication via message-passing. The experiment investigated the effects of various high-level control strategies on the quality of the problem solution, the speedup of the overall system performance as a function of the number of processors in the grid, and some of the issues in implementing and debugging a knowledge-based system on a message-passing multiprocessor system. This report describes the software and (simulated) hardware components of the experiment and present the qualitative and quantitative experimental results. (Author)
The knowledge engineer as student : metacognitive bases for asking good questions by William J Clancey( Book )

2 editions published in 1987 in English and held by 4 WorldCat member libraries worldwide

Knowledge engineers are efficient, active learners. They systematically approach domains and acquire knowledge to solve routine, practical problems. By modeling their methods, we may develop a basis for teaching other students how to direct their own learning. In particular, a knowledge engineer is good at detecting gaps in knowledge base and asking focused questions to improve an expert system's performance. This ability stems from domain. General knowledge, about: problem-solving procedures, the categorization of routine problem-solving knowledge, and domain and task differences. This paper studies these different forms of metaknowledge, and illustrates its incorporation in an intelligent tutoring system. A model of learning is presented that describes how the knowledge engineer detects problem solving failures and tracks them back to gaps in domain knowledge, which are then reformulated as questions to ask a teacher. We describe how this model of active learning is being developed and tested in a knowledge acquisition program for an expert system. Keywords: Learning, Knowledge engineering, Knowledge acquisition, Metaknowledge
CAREL : a visible distributed Lisp by Byron Davies( Book )

1 edition published in 1986 in English and held by 4 WorldCat member libraries worldwide

CAREL is a Lisp implementation designed to be a high-level interactive systems programming language for a distributed-memory multiprocessor. CAREL insulates the user from the machine language of the multiprocessor architecture, but still makes it possible for the user to specify explicitly the assignment of tasks to processors in the multiprocessor network. CAREL has been implemented to run on the T1 Explorer Lisp machine using Stanford's CARE multiprocessor simulator Delagi 86. CAREL is more than a language: real-time graphical displays provided by the CARE simulator make CAREL a novel graphical programming environment for distributed computing. CAREL enables the user to create programs interactively and then watch them run on a network of simulated processors. As a CAREL program executes, the CARE simulator graphically displays the activity of the processors and the transmission of data through the network. Using this capability, CAREL has demonstrated its utility as an educational tool for multiprocessor computing
Artificial intelligence technical reports : Knowledge Systems Laboratory, Stanford University, 1977-1985( Book )

1 edition published in 1985 in English and held by 4 WorldCat member libraries worldwide

Mapping explanation-based generalization onto Soar by Paul S Rosenbloom( Book )

2 editions published in 1986 in English and held by 4 WorldCat member libraries worldwide

Explanation-based generalization (EBG) is a powerful approach to concept formation in which a justifiable concept definition is acquired from a single training example and an underlying theory of how the example is an instance of the concept. Soar is an attempt to build a general cognitive architecture combining general learning, problem solving, and memory capabilities. It includes an independently developed learning mechanism, called chunking, that is similar to but not the same as explanation-based generalization. In this article we clarify the relationship between the explanation-based generalization framework and the Soar/chunking combination by showing how the EBG framework maps onto Soar, how several EBG concept-formation tasks are implemented in Soar, and how several EBG concept-formation tasks are implemented in Soar, and how the Soar approach suggests answers to some of the outstanding issues in explanation-based generalization. (KR)
Qualitative student models by William J Clancey( Book )

3 editions published in 1986 in English and held by 4 WorldCat member libraries worldwide

The concept of a qualitative model is used as the focus of this review of qualitative student models in order to compare alternative computational models and to contrast domain requirements. The report is divided into eight sections: (1) Origins and Goals (adaptive instruction, qualitative models of processes, components of an artificial intelligence based instructional program, contrast with traditional computer assisted instruction, historical progression, and the range of existing programs); (2) Scope of the Review (review topics, important distinctions, relation to cognitive psychology, relation to other areas of artificial intelligence, and special emphases); (3) The Role of Qualitative Models in Instruction (explanation of qualitative models, situation-specific models, simulation or executable models, representation requirements, and the central role of diagnosis); (4) Student Model Assessments; (5) Contrast between Formal and Physical Domains (written notation and operators, subtraction compared to medical diagnosis, artifactual vs. Natural functionality, and algorithmic vs. Heuristic inference); (6) Types of Qualitative Process Models (process models of reasoning, behavioral vs. Functional process models, levels of abstraction, idealized competence vs. Individual models, computational representations for qualitative models, classification vs. Simulation models of bugs, overlay vs. Bug models, and pragmatic considerations); (7) Constructing Situation-Specific Process Models (simulate variations of the inference procedure, simulate variations in the program's general domain model, and derivation of the inference procedure); and (8) Conclusions (recapitulation, methodology, state of the art, and trends). A brief guide to the literature is included. (191 references) (mes)
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Alternative Names
Knowledge Systems Laboratory


Stanford University Department of Computer Science Knowledge Systems Laboratory

English (47)