Technical report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology.

The AI field needs major breakthroughs in its thinking to achieve continuous, sensory-gathered, machine learning from the environment on unlimited subjects. The way motivate such dramatic progress is to articulate and endorse research goals for machine behavior so ambitious that limited-domain, problemsolving knowledge representation methods are disqualified at the outset, thus forcing ourselves to produce valuable new "thoughtware." After exploring why the tendency to associate intelligence with problem-solving may be a mental roadblock to further progress in AI science, some preliminary thinking tools are introduced more suitable for sensory learning machine research. These include lifelong sensorimotor data streams, representation as a symbolic recording process, knowledge transmission, and the totality of knowledge.

Canadians have made many contributions to artificial intelligence over the years. This article presents a summary of current research in artificial intelligence in Canada and acquaints readers with the Canadian organization for artificial intelligence -- the Canadian Society for the Computational Studies of Intelligence / Societe Canadienne pour l' Etude de l'Intelligence par Ordinateur (CSCSI/ SCEIO).

Between these dates, Charlie organized an Applied Physics Laboratory and became interested in "learning machines" and "self-organizing systems." That interest launched a group that ultimately grew into a major world center of artificial intelligence research - a center that has endured twenty-five years of boom and bust in fashion, has "graduated" over a hundred AI research professionals, and has generated ideas and programs resulting in new products and companies as well as scientific articles, books, and this particular collection itself.

Charles A. Rosen came to SRI in 1957. I arrived in 1961. Between these dates, Charlie organized an Applied Physics Laboratory and became interested in "learning machines" and "self-organizing systems." That interest launched a group that ultimately grew into a major world center of artificial intelligence research - a center that has endured twenty-five years of boom and bust in fashion, has "graduated" over a hundred AI research professionals, and has generated ideas and programs resulting in new products and companies as well as scientific articles, books, and this particular collection itself.

IEEE Transactions on Information Theory, IT-30 (4), 629-36.

"Chunks have long been proposed as a basic organizational unit for human memory. More recently chunks have been used to model human learning on simple perceptual-motor skills. In this paper we describe recent progress in extending chunking to be a general learning mechanism by implementing it within a general problem solver. Using the Soar problem-solving architecture, we take significant steps toward a general problem solver that can learn about all aspects of its behavior. We demonstrate chunking in Soar on three tasks: the Eight Puzzle, Tic-Tat-Toe, and a part of the RI computer-configuration task. Not only is there improvement with practice, but chunking also produces significant transfer of learned behavior, and strategy acquisition."Proceedings of the AAAi-84 National Conference. AAAI, University of Texas at Austin, TX, August, 1984.

In AAAI-84, pp. 251–254.

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

The complete book in a single file.

Artificial Intelligence, as a maturing scientific/engineering discipline, is beginning to find its niche among the variety of subjects that are relevant to intelligent, perceptive behavior. A view of AI is presented that is based on a declarative representation of knowledge with semantic attachments to problem-specific procedures and data structures. Several important challenges to this view are briefly discussed. It is argued that research in the field would be stimulated by a project to develop a computer individual that would have a continuing existence in time.