AI
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Acquiring Strategic Knowledge From Experts
Thomas R. Gruber (1988). Acquiring Strategic Knowledge from Experts. International Journal of Man-Machine Studies, Volume 29 , Issue 5 (November 1988), pp. 579-597. Reprinted in The Foundations of Knowledge Acquisition, 1990, pp. 115-133, Academic Press, ISBN:0-12-115922-1.
Abstract: This paper presents an approach to the problem of acquiring strategic knowledge from experts. Strategic knowledge is used to decide what course of action to take, when there are confiicting criteria to satisfy and the effects of actions are not known in advance. We show how strategic knowledge challenges the current approaches to knowledge acquisition: knowledge engineering, interactive tools for experts, and machine learning. We present a knowledge acquisition methodology embodied by an interactive tooi that draws from each approach, automating much of what is currently performed by knowledge engineers, and synthesizing interactive and automatic learning techniques. The technique for eliciting strategic knowledge from experts and transforming it into an executable form addresses the technical problems of operationalization, encoding examples, biasing generalization, and the new terms problem.
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A Method for Acquiring Strategic Knowledge
Thomas R. Gruber (1989). A Method for Acquiring Strategic Knowledge. Knowledge Acquisition, Volume 1 , Issue 3 (September 1989), pp. 255-277.
Original Abstract: In this article we present an automated method for acquiring strategic knowledge from experts. Strategic knowledge is used by an agent to decide what action to perform next, where actions effect both the agent’s beliefs and the state of the external world. Strategic knowledge underlies expertise in many tasks, yet it is difficult to acquire from experts and is generally treated as an implementation problem. The knowledge acquisition method consists of the design of an operational representation for strategic knowledge, a technique for eliciting it from experts, and an interactive assistant that manages a learning dialog with the expert. The assistant elicits cases of expert-justified strategic decisions and generalizes strategic knowledge with syntactic induction guided by the expert. The knowledge acquisition method derives its power and limitations from the way in which strategic knowledge is represented and applied.
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Thomas R. Gruber (1993). Model formulation as a problem-solving task: computer-assisted engineering modeling. In Knowledge Acquisition as Modeling, 1993, pp. 105-127, John Wiley & Sons, Inc. New York, NY, USA . ISBN:0-471-59368-0.
A meta-learning paper of the day, describing a knowledge-based theory for building knowledge based systems.
Original Abstract: A central purpose of knowledge acquisition technology is to assist with the formulation of domain models that underlie knowledge systems. In this article we examine the model formulation process itself as a problem‐solving task. Drawing from AI research in qualitative reasoning about physical systems, we characterize the model formulation task in terms of the inputs, the reasoning subtasks, and the knowledge needed to perform the problem solving. We describe the elements of a high‐level representation of modeling knowledge, and techniques for providing intelligent assistance to the model builder. Applying the results from engineering modeling to knowledge acquisition in general, we identify properties of the representation that facilitate the construction of knowledge systems from libraries of reusable models.
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The Acquisition of Strategic Knowledge
Thomas R. Gruber (1989). The Acquisition of Strategic Knowledge. San Diego: Academic press, 1989. ISBN:0-12-304754-4.
The PhD thesis turned into a book.
Publisher’s abstract:
The Acquisition of Strategic Knowledge deals with the automation of the acquisition of strategic knowledge and describes a knowledge acquisition program called ASK, which elicits strategic knowledge from domain experts and puts it in operational form. This book explores the dynamics of intelligent systems and how the components of knowledge systems (including a human expert) interact to produce intelligence. Emphasis is placed on how to represent knowledge that experts require to make decisions about actions. The move toward abstract tasks and how tasks are solved are discussed, along with their implications for knowledge acquisition, particularly the acquisition of expert strategies.
This book is comprised of eight chapters and begins with an overview of the knowledge acquisition problem for strategic knowledge, as well as the relevance of strategic knowledge to artificial intelligence. The next chapter describes a dialog session between the ASK knowledge acquisition assistant and the user (“”the expert””). The discussion then turns to software architecture with which to represent strategic knowledge; design and implementation of an assistant for acquiring strategic knowledge; and approaches to knowledge acquisition. Two applications of the ASK system are considered: to evaluate the usability of the elicitation technique with real users and to test the adequacy of the strategy rule representation upon which the approach is dependent. The scope of ASK, its sources of power, and its underlying assumptions are also outlined.
This monograph will be a valuable resource for knowledge systems designers and those interested in artificial intelligence and expert systems.
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Thomas R. Gruber (1991). Interactive Acquisition of Justifications: Learning “Why” by Being Told “What.” IEEE Expert, 6(4): 65-75, August 1991.
In this paper I describe an approach to automated knowledge acquisition in which users specify desired system behavior by constructing justifications of examples. Justifications are explanations of why example behaviors are appropriate in given situations. I analyze the problem of acquiring justifications, showing how current knowledge acquisition techniques are best suited for asking what-questions while justifications are naturally viewed as answers to why-questions. I sketch a new approach for acquiring justifications that transforms why-questions into what-questions, borrowing the sources of power of existing techniques. In this approach, users construct justifications by selecting facts that specify what is relevant in a situation from a space of facts provided by the elicitation tool. Justifications are then used to create operational mappings from situations to intended outcomes. I show how the approach is applied to two different knowledge acquisition problems: the acquisition of diagnostic strategy and the acquisition of design rationale. I conclude by identifying common characteristics of the two applications and discuss how their design distributes the cognitive load between human and machine.
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Thomas R. Gruber (1989). Automated Knowledge Acquisition for Strategic Knowledge. Machine Learning, Volume 4 , Issue 3-4 (December 1989), pp. 293 – 336.
Original Abstract: Strategic knowledge is used by an agent to decide what action to perform next, where actions have consequences external to the agent. This article presents a computer-mediated method for acquiring strategic knowledge. The general knowledge acquisition problem and the special difficulties of acquiring strategic knowledge are analyzed in terms of representation mismatch: the difference between the form in which knowledge is available from the world and the form required for knowledge systems. ASK is an interactive knowledge acquisition tool that elicits strategic knowledge from people in the form of justifications for action choices and generates strategy rules that operationalize and generalize the expert’s advice. The basic approach is demonstrated with a human-computer dialog in which ASK acquires strategic knowledge for medical diagnosis and treatment. The rationale for and consequences of specific design decisions in ASK are analyzed, and the scope of applicability and limitations of the approach are assessed. The paper concludes by discussing the contribution of knowledge representation to automated knowledge acquisition.
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Nature, Nurture, and Knowledge Acquisition
Thomas R. Gruber (2013). Nature, Nurture, and Knowledge Acquisition. International Journal Human-Computer Studies, Vol. 71, Issues 2, February 2013, pp.191-194.
The nature vs. nurture dualism has framed the modern conversation in biology and psychology. There is an analogous distinction for Knowledge Acquisition and Artificial Intelligence. In the context of building intelligent systems, Nature means acquiring knowledge by being programmed or modeled that way. Nurture means acquiring knowledge by machine learning from data and information in the world. This paper develops the nature/nurture analogy in light of the history of Knowledge Acquisition, the current state of the art, and the future of intelligent machines learning from human knowledge.
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Patrice O. Gautier and Thomas R. Gruber (1993). Generating Explanations of Device Behavior Using Compositional Modeling and Causal Ordering. Proceedings of the Eleventh National Conference on Artificial Intelligence, Washington, D.C., AAAI Press/The MIT Press, 1993.
Original Abstract: Generating explanations of device behavior is a long-stand ing goal of AI research in reasoning about physical systems. Much of the relevant work has concentrated on new methods for modeling and simulation, such as qualitative physics, or on sophisticated natural language generation, in which the device models are specially crafted for explanatory purposes. We show how two techniques from the modeling research—compositional modeling and causal ordering—can be effectively combined to generate natural language explanations of device behavior from engineering models. The explanations offer three advances over the data displays produced by conventional simulation software: (1) causal interpretations of the data, (2) summaries at appropriate levels of abstraction (physical mechanisms and component operating modes), and (3) query-driven, natural language summaries. Furthermore, combining the compositional modeling and causal ordering techniques allows models that are more scalable and less brittle than models designed solely for explanation. However, these techniques produce models with detail that can be distracting in explanations and would be removed in hand-crafted models (e.g., intermediate variables). We present domain-independent filtering and aggregation techniques that overcome these problems.
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Thomas R. Gruber and Patrice O. Gautier. (1993). Machine-generated explanations of engineering models: A compositional modeling approach. Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France, pages 1502-1508, San Mateo, CA: Morgan Kaufmann, 1993.
Original Abstract: We describe a method for generating causal explanations, in natural language, of the simulated behavior of physical devices. The method is implemented in DME, a system that helps formulate mathematical simulation models from a library of model fragments using a Compositional Modeling approach. Because explanations are generated from models that are dynamically constructed from modular pieces, several of the limitations of conventional explanation techniques are overcome. Since the explanation system has access to the derivation of mathematical equations from the original model specification, the system can explain low-level quantitative behavior predicted by conventional simulation techniques in terms of salient behavioral abstractions such as physical processes, idealized components, and operating modes. Instead of relying on ad hoc causal models, crafted specifically for the ex- planation task, the program infers causal relationships among parameters in a constraint-based equation model. Rather than using canned, top-down templates, the text generator composes textual annotations associated with individual model fragments into coherent sentences. We show how these techniques can be combined to produce a variety of explanations about simulated systems.
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Model-based Virtual Document Generation
Thomas Gruber, Sunil Vemuri, and James Rice (1995). Model-Based Virtual Document Generation. International Journal of Human-Computer Studies, Volume 46 , Issue 6 (June 1997). Special issue: innovative applications of the World Wide Web. ISSN:1071-5819.
Describes the use of the web as a medium for virtual documents that generate natural language explanations of how things work.
Original Abstract: Virtual documents are hypermedia documents that are generated on demand in response to reader input. This paper describes a virtual document application that generates natural language explanations about the structure and behavior of electromechanical systems. The application structures the interaction with the reader as a question-answer dialog. Each “page” of the hyperdocument is the answer to a question, and each “link” is another question that leads to another answer. Unlike conventional hypertext documentation, the system dynamically constructs answers to questions from formal engineering models.
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