A memorial tribute to AI researcher Jonathan King who died of cancer in 1991.

Case-based reasoning provides both a methodology for building systems and a cognitive model of people. It is consistent with much that psychologists have observed in the natural problem solving people do. Psychologists have also observed, however, that people have several problems in doing analogical or case-based reasoning. Although they are good at using analogs to solve new problems, they are not always good at remembering the right ones. However, computers are good at remembering. I present case-based decision aiding as a methodology for building systems in which people and machines work together to solve problems. The case-based decision-aiding system augments the person's memory by providing cases (analogs) for a person to use in solving a problem. The person does the actual decision making using these cases as guidelines. I present an overview of case-based decision aiding, some technical details about how to implement such systems, and several examples of case-based systems.

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 Artificial Intelligence, Simulation, and Modeling

Review of Expert Systems in Business: A Practical Approach

Review of Genetic Algorithms in Search, Optimization, and Machine Learning

In solving a new problem, we rely on past episodes. 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. The research issues for case-based reasoning include the representation of episodic knowledge, memory organization, indexing, case modification, and learning. In this article, I review the history of case-based reasoning, including research conducted at the Yale AI Project and elsewhere.