Patrick Henry Winston

Bio: Patrick Henry Winston is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Natural language & Narrative. The author has an hindex of 27, co-authored 91 publications receiving 6381 citations.

Learning Structural Descriptions From Examples

Abstract: Massachusetts Institute of Technology. Dept. of Electrical Engineering. Thesis. 1970. Ph.D.

Learning and reasoning by analogy

Abstract: We use analogy when we say something is a Cinderella story and when we learn about resistors by thinking about water pipes. We also use analogy when we learn subjects like economics, medicine, and law. This paper presents a theory of analogy and describes an implemented system that embodies the theory. The specific competence to be understood is that of using analogies to do certain kinds of learning and reasoning. Learning takes place when analogy is used to generate a constraint description in one domain, given a constraint description in another, as when we learn Ohm's law by way of knowledge about water pipes. Reasoning takes place when analogy is used to answer questions about one situation, given another situation that is supposed to be a precedent, as when we answer questions about Hamlet by way of knowledge about Macbeth.

Method and apparatus for utilizing annotations to facilitate computer retrieval of database material

Abstract: A method and apparatus for computer retrieval of database material which may be text, computer programs, graphics, audio, object classes, action specifications or other material which may be machine stored. Annotations are provided for at least selected database subdivisions, preferably with natural language questions, assertions or noun phrases or some combination/collection thereof. However, the annotations may also initially be generated in a structured form. Annotations are, if required, converted to a structured form and are stored in that form along with connections to corresponding subdivisions. Searching for relevant subdivisions involves entering a query in natural language or structured form, converting natural language queries to structured form, matching the structured form query against stored annotations and retrieving database subdivisions connected to matched annotations. The annotation process may be aided by utilizing various techniques for automatically or semiautomatically generating the annotations.

Method and apparatus for generating and utlizing annotations to facilitate computer text retrieval

Abstract: A method and apparatus for computer text retrieval which involves annotating at least selected text subdivisions, preferably with natural language questions, assertions or noun phrases. However, the annotations may also initially be generated in a structured form. Annotations are, if required, converted to a structured form and are stored in that form. Searching for relevant text subdivisions involves entering a query in natural language or structured form, converting natural language queries to structured form, matching the structured form query against stored annotations and retrieving text subdivisions connected to matched annotations. The annotation process may be aided by utilizing various techniques for automatically or semiautomatically generating the annotations.

Induction of Decision Trees

Abstract: The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty—and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition—in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Fuzzy Set Theory - and Its Applications

Abstract: Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.

A framework for representing knowledge

Abstract: : A partial theory is presented of thinking, combining a number of classical and modern concepts from psychology, linguistics, and AI. In a new situation one selects from memory a structure called a frame: a remembered framework to be adapted to fit reality by changing details as necessary, and a data-structure for representing a stereotyped situation. Attached to each frame are several kinds of information -- how to use the frame, what one can expect to happen next, and what to do if these expectations are not confirmed. The report discusses collections of related frames that are linked together into frame-systems.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Abstract: Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.