But even if it isn't way, and addresses significant tasks.

Engineering and scientific education condition us to expect everything, including intelligence, to have a simple, compact explanation. Accordingly, when people new to AI ask "What's AI all about," they seem to expect an answer that defines AI in terms of a few basic mathematical laws. Today, some researchers who seek a simple, compact explanation hope that systems modeled on neural nets or some other connectionist idea will quickly overtake more traditional systems based on symbol manipulation. Others believe that symbol manipulation, with a history that goes back millennia, remains the only viable approach. Marvin Minsky subscribes to neither of these extremist views. Instead, he argues that AI must use many approaches. AI is not like circuit theory and electromagnetism. There is nothing wonderfully unifying like Kirchhoff's laws are to circuit theory or Maxwell's equations are to electromagnetism. Instead of looking for a "right way," the time has come to build systems out of diverse components, some connectionist and some symbolic, each with its own diverse justification." - Patrick Winston

Machine, I raised (much more playfully) one of the questions David M. West and Larry E. Travis raise in their important article, "The Computational Metaphor and Artificial Intelligence". AI might CA) has added a download microcode FL) has developed an expert system have gone off on the wrong track, enhancement to its Hi-Track expert to set its prices nationwide for Alamo's rather like Columbus believing he'd system. The enhancement will allow rental cars. The embedded system analyzes discovered the Indies. Columbus Hi-Track to remotely identify and the competition's prices, compares hadn't discovered the Indies; in fact solve potential problems in a customer's them to Alamo's, and then he'd stumbled on something as least storage subsystem, over the telephone.

The Board of Directors the address given here.

Review of Expert Systems in Business: A Practical Approach

This article analyzes an attempt to use computing technology, including AI, to improve the combat readiness of a U.S. Navy aircraft carrier. The method of introducing new technology, as well as the reaction of the organization to the use of the technology, is examined to discern the reasons for the rejection by the carrier's personnel of a technically sophisticated attempt to increase mission capability. This effort to make advanced computing technology, such as expert systems, an integral part of the organizational environment and, thereby, to significantly alter traditional decision-making methods failed for two reasons: (1) the innovation of having users, as opposed to the navy research and development bureaucracy, perform the development function was in conflict with navy operational requirements and routines and (2) the technology itself was either inappropriate or perceived by operational experts to be inappropriate for the tasks of the organization. Finally, this article suggests those obstacles that must be overcome to successfully introduce state-of-the-art computing technology into any organization.

The goal of this research is to learn to control the attitude of an orbiting satellite. To this end, we are investigating the possibility of using adaptive controllers for such tasks. Laboratory tests have suggested that rule-based methods can be more robust than systems developed using traditional control theory. The BOXES learning system, which has already met with success in simulated laboratory tasks, is an effective design framework for this new exercise.

The US Army has installed PRIDE Merlin is an expert system developed (Pulse Radar Intelligent Diagnostic at Hewlett Packard's Networked Environment), a diagnostic expert Computer Manufacturing Operation system developed by Carnegie Group (Roseville, CA) to forecast the factory's (Pittsburgh, PA), in Saudi Arabia in product demand. Lucid (Menlo Park, CA), producer of American Airlines (Dallas, TX) has the Lucid Common Lisp language, developed an expert system - Maintenance has acquired Peritus, a producer of Operation Control Advisor C/C and FORTRAN compilers. Consolidated Edison (New York, Nova Technology (Bethesda, MD), a NY) has developed the SOCCS Alarm new company founded by Naval Advisor, an expert system that recommends Research Center scientist Harold Szu, operator actions required plans to commercialize neural networks to maintain the necessary and continuous made from high-performance power supply to its customers. Kurzweil AI (Waltham, MA) has Inference (El Segundo, CA) has received a federal grant to develop named Peter Tierney CEO and president. VoiceGI, a voice-activated reporting Tierney was formerly VP of and database management system marketing at Oracle.

This article reports on experiments performed using a black-box simulation of a spacecraft. The goal of this research is to learn to control the attitude of an orbiting satellite. The space-craft must be able to operate with minimal human supervision. To this end, we are investigating the possibility of using adaptive controllers for such tasks. Laboratory tests have suggested that rule-based methods can be more robust than systems developed using traditional control theory. The BOXES learning system, which has already met with success in simulated laboratory tasks, is an effective design framework for this new exercise.

Expertise comprises experience. In solving a new problem, we rely on past episodes. We need to remember what plans succeed and what plans fail. We need to know how to modify an old plan to fit a new situation. Case-based reasoning is a general paradigm for reasoning from experience. It assumes a memory model for representing, indexing, and organizing past cases and a process model for retrieving and modifying old cases and assimilating new ones. Case-based reasoning provides a scientific cognitive model. The research issues for case-based reasoning include the representation of episodic knowledge, memory organization, indexing, case modification, and learning. In addition, computer implementations of case-based reasoning address many of the technological shortcomings of standard rule-based expert systems. These engineering concerns include knowledge acquisition and robustness. In this article, I review the history of case-based reasoning, including research conducted at the Yale AI Project and elsewhere.