In 1940, a 20-year-old science fiction fan from Brooklyn found that he was growing tired of stories that endlessly repeated the myths of Frankenstein and Faust: Robots were created and destroyed their creator; robots were created and destroyed their creator; robots were created and destroyed their creator-ad nauseum. So he began writing robot stories of his own. "[They were] robot stories of a new variety," he recalls. "Never, never was one of my robots to turn stupidly on his creator for no purpose but to demonstrate, for one more weary time, the crime and punishment of Faust. My robots were machines designed by engineers, not pseudo-men created by blasphemers. My robots reacted along the rational lines that existed in their'brains' from the moment of construction. " In particular, he imagined that each robot's artificial brain would be imprinted with three engineering safeguards, three Laws of Robotics: 1. A robot may not injure a human being or, through inaction, allow a human being to come to harm. 2. A robot must obey the orders given it by human beings except where such orders would conflict with the first law. The young writer's name, of course, was Isaac Asimov (1964), and the robot stories he began writing that year have become classics of science fiction, the standards by which others are judged. Indeed, because of Asimov one almost never reads about robots turning mindlessly on their masters anymore. But the legends of Frankenstein and Faust are subtle ones, and as the world knows too well, engineering rationality is not always the same thing as wisdom. M Mitchell Waldrop is a reporter for Science Magazine, 1333 H Street N.W., Washington D C. 2COO5. Reprinted by permission of the publisher.

"Knowledge-Based Tutoring describes the advantages and difficulties of adapting an expert system for use in teaching and problem solving. In this case the well-known rule-based expert system, MYCIN, which has been widely used in medical artificial intelligence to do infectious disease diagnosis and therapy selection, is used as a base for the instructional program GUIDON. MYCIN's rules are interpreted by GUIDON in order to evaluate a student's problem solving and provide assistance as the student gathers information about a patient and makes a diagnosis. The book describes what GUIDON does, how it is constructed, and the benefits and limitations of its design."

Chapman and Hall. See also: Influence diagrams for Bayesian decision analysis, European Journal of Operational Research, Volume 40, Issue 3, 15 June 1989, Pages 363–376 (http://www.sciencedirect.com/science/article/pii/0377221789904293). Bayesian Decision Analysis: Principles and Practice, Cambridge University Press, 2010 (https://books.google.com/books/about/Bayesian_Decision_Analysis.html?id=O1lXnQAACAAJ).

We distinguish diagrammatic from sentential paper-and-pencil representationsof information by developing alternative models of information-processing systems that are informationally equivalent and that can be characterized as sentential or diagrammatic. Sentential representations are sequential, like the propositions in a text. Dlogrammotlc representations ore indexed by location in a plane. Dio-grommatic representations also typically display information that is only implicit in sententiol representations and that therefore has to be computed, sometimes at great cost, to make it explicit for use. We then contrast the computational efficiency of these representotions for solving several illustrative problems in mothe-matics and physics. When two representotions are informationally equivolent, their computational efficiency depends on the information-processing operators that act on them. Two sets of operators may differ in their copobilities for recognizing patterns, in the inferences they con carry out directly, and in their control strategies (in portitular. Diogrommotic ond sentential representations sup port operators that differ in all of these respects. Operators working on one representation moy recognize feotures readily or make inferences directly that are difficult to realize in the other representation. Most important, however, are differences in the efficiency of scorch for information and in the explicitness of information. In the representotions we call diagrammatic. Therefore problem solving con proceed through o smooth traversal of the diagram, and may require very little search or computation of elements that hod been implicit. "a picture is worth 10,OOO words" is a Chinese proverb. On inquiry, we find that the Chinese seem not to have heard of it, but the proverb is certainly widely known and widely believed in our culture. To understand why it is advantageous to use diagrams-and when it is-we must find some way to contrast diagrammatic and non-diagrammatic representations in an information-processing system.

In this paper, the reasoning and planning capabilities of an autonomous mobile robot are described; The reasoning system that controls the robot is designed to exhibit the kind of behavior expected of a rational agent, and is endowed with the psychological attitudes of belief, desire, and intention. Because these attitudes are explicitly represented, they can be manipulated and reasoned about, resulting in complex goal-directed and reflective behaviors. Unlike most planning systems, the plans or intentions formed by the robot need only be partly elaborated before it decides to act. This allows the robot to avoid overly strong expectations about the environment, overly constrained plans of action, and other forms of overcommitment common to previous planners. In addition, the robot is continuously reactive and has the ability to change its goals and intentions as situations warrant. The system has been tested with SRI's autonomous robot (Flakey) in a space station scenario involving navigation and the performance of emergency tasks. 1

In Automatic Control—World Congress, 1987: Selected Papers from the 10th Triennial World Congress of the International Federation of Automatic Control, pp. 221–226.

The system presented here shows how Soar, an architecture for general problem solving and learning, can acquire general search-control knowledge from outside guidance. The guidance can be either direct advice about what the system should do, or a problem that illustrates a relevant idea. The system makes use of the guidance by first formulating an appropriate goal for itself. In the process of achieving this goal, it learns general search-control chunks. In the case of learning from direct advice, the goal is to verify that the advice is correct. The verification allows the system to obtain general conditions of applicability of the advice, and to protect itself from erroneous advice. The system learns from illustrative problems by setting the goal of solving the problem provided. It can then transfer the lessons it learns along the way to its original problem. This transfer constitutes a rudimentary form of analogy.

Review of Artificial Intelligence for Microcomputers: The Guide for Business Decision Makers

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.

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.