Patrick Henry Winston

Learning and reasoning by analogy

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 Ohms 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.

Learning New Principles from Precedents and Exercises.

Abstract Much learning is done by way of studying precedents and exercises. A teacher supplies a story, gives a problem, and expects a student both to solve a problem and to discover a principle. The student must find the correspondence between the story and the problem, apply the knowledge in the story to solve the problem, generalize to form a principle, and index the principle so that it can be retrieved when appropriate. This sort of learning pervades Management, Political Science, Economics, Law, and Medicine as well as the development of common-sense knowledge about life in general. This paper presents a theory of how it is possible to learn by precedents and exercises and describes an implemented system that exploits the theory. The theory holds that causal relations identify the regularities that can be exploited from past experience, given a satisfactory representation for situations. The representation used stresses actors and objects which are taken from English-like input and arranged into a kind of semantic network. Principles emerge in the form of production rules which are expressed in the same way situations are.

Variable precision logic

Abstract Variable precision logic is concerned with problems of reasoning with incomplete information and resource constraints. It offers mechanisms for handling trade-offs between the precision of inferences and the computational efficiency of deriving them. Two aspects of precision are the specificity of conclusions and the certainty of belief in them; we address primarily certainty and employ censored production rules as an underlying representational and computational mechanism. These censored production rules are created by augmenting ordinary production rules with an exception condition and are written in the form “if A then B unless C”, where C is the exception condition. From a control viewpoint, censored production rules are intended for situations in which the implication A ⇒ B holds frequently and the assertion C holds rarely. Systems using censored production rules are free to ignore the exception conditions when resources are tight. Given more time, the exception conditions are examined, lending credibility to high-speed answers or changing them. Such logical systems, therefore, exhibit variable certainty of conclusions, reflecting variable investment of computational resources in conducting reasoning. From a logical viewpoint, the unless operator between B and C acts as the exclusive-or operator. From an expository viewpoint, the “if A then B” part of censored production rule expresses important information (e.g., a causal relationship), while the “unless C” part acts only as a switch that changes the polarity of B to ¬B when C holds. Expositive properties are captured quantitatively by augmenting censored rules with two parameters that indicate the certainty of the implication “if A then B”. Parameter δ is the certainty when the truth value of C is unknown, and γ is the certainty when C is known to be false.

The AI business: commercial uses of artificial intelligence

What is the bottom line on Artificial Intelligence? The AI Business offers a comprehensive summary of the commercial picture, present and future, for Artificial Intelligence in the computer industry, medicine, the oil industry, and electronic design. AIs brightest and best -- financiers, researchers, and users -- analyze current projects, speculate on trends in factory automation, compare research in Japan and the U.S., and note the pros and cons of investment opportunities.Contents: Expert Systems. Amplifying Expertise with Expert Systems, Randall Davis (MIT). XCON: An Expert Configuration System at Digital Equipment Corporation, Arnold Kraft (DEC). DIPMETER ADVISOR: An Expert Logo Analysis System at Schlumberger, James D. Baker (Schlumberger). CADUCEUS: An Experimental Expert System for Medical Diagnosis, Harry E. Pople, Jr. (University of Pittsburgh). The Low Road, the Middle Road, and the High Road, John Seely Brown (Xerox).Work and Play. Inventing the Future, Alan Kay (Atari). The Engineers Apprentice, Aryeh Finegold (Daisy Systems Corporation). The Programmers Apprentice, Charles Rich (MIT). Intelligent Advisory Systems, Roger Schank (Cognitive Systems, Inc. and Yale University). Natural Language Front Ends, Larry R. Harris (Artificial Intelligence Corporation).Robotics. Intelligent Robots: Connecting Perception to Action, ). Michael Brady (MIT). Intelligent Robots: Moving toward Megassembly, Philippe Villers (Automatix, Inc.). Intelligent Robots: Myth or Reality, Paul M. Russo (GE).Today and Tomorrow. The Problems and the Promise, Marvin Minsky (MIT). An Investment Opportunity? Frederick R. Adler (Adler & Company). Financing the Future, William H. Janeway (F. Eberstadt & Co., Inc.) From the Blocks World to the Business World, Patrick H. Winston and Karen A. Prendergast (MIT). How to Learn More.

Achieving human-level intelligence through integrated systems and research: introduction to this special issue

This special issue is based on the premise that in order to achieve human-level artificial intelligence researchers will have to find ways to integrate insights from multiple computational frameworks and to exploit insights from other fields that study intelligence. Articles in this issue describe recent approaches for integrating algorithms and data structures from diverse subfields of AI. Much of this work incorporates insights from neuroscience, social and cognitive psychology or linguistics. The new applications and significant improvements to existing applications this work has enabled demonstrates the ability of integrated systems and research to continue progress towards human-level artificial intelligence.

Massively parallel symbolic induction of protein structure/function relationships

Reports the development and implementation of efficient algorithms for several symbolic machine learning induction operators on a massively parallel computer. The authors invoke these operators as hardware induction subroutines under the control of a higher-level front-end LISP program. For them, the key contribution of this work is its demonstration of the scalability of the algorithms involved. The time complexity of the induction algorithms is essentially independent of the total size of the instance data pool, with essentially linear space (hardware) complexity. Everything described has been implemented in Common LISP or PARIS. The PARIS portion runs on a CM-2 Connection Machine. The system (ARIEL) has been applied to the DNA polymerases and to the transcriptional activators by domain experts.<<ETX>>

Computational Models of Narrative: Review of a Workshop

On October 8-10, 2009 an interdisciplinary group met at the Wylie Center in Beverley, Massachusetts to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in computational modeling is described by three different levels of representation; (2) there is a paucity of studies at the highest, most abstract level aimed at inferring the meaning or message of the narrative; and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank.

Guest Editors' Introduction: The New Frontier of Human-Level Artificial Intelligence

Within the field of human-level intelligence, researchers are combining a variety of approaches toward the goals of human-like breadth, flexibility, and resilience for artificial intelligence systems. Each of the four papers in this special issue brings a different background and perspective on the subject, and hence a different technical approach.

Model-based Story Summary

A story summarizer benefits greatly from a reader model because a reader model enables the story summarizer to focus on delivering useful knowledge in minimal time with minimal eort. Such a

On Computing Machinery and Intelligence

The Turing Test has been part of the lexicon of artificial intelligence ever since Turing proposed it in his famous paper, “Computing Machinery and Intelligence” (1950). Close reading suggests, however, that Turing’s real purpose in writing the paper was, firstly, to attack the arguments of skeptics so as to establish that there is no reason to believe computers cannot be intelligent, and secondly, to propose a program of research. Today, Turing would likely have structured his thinking differently, and perhaps focused on different questions, perhaps the questions on which I focus: What is it that makes us different from all other species? And what is it that we have in common with other species that makes the difference important? I conclude that story understanding makes us different and that story understanding rests on directed perception. I elaborate on story understanding, explaining how a simple substrate of English analysis, common sense inference, and concept search enable the Genesis story understanding system to demonstrate a range of competences, including culturally grounded story interpretation and question-driven analysis. All this leads to a discussion of open questions and a reassessment of Turing’s paper’s fundamental contribution.