CMU-CS-85-115, Carnegie Mellon University. Reprinted in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M., (Eds.), Machine Learning: An Artificial Intelligence Approach, volume 2, chapter 14, pages 371-392. Morgan Kaufmann Publishers. Derivational analogy, a method of solving problems based on the transfer of past experience to new probiem situations, is discussed in the context of other general approaches to problem solving. The experience transfer process consists of recreating lines of reasoning, including decision sequences and accompanying justifications, that proved effective in solving particular problems requiring similar initial analysis. The role of derivational analogy in case-based reasoning and in automated expertise acquisition is discussed.

J. International Computer Chess Association, May, 131–139.

MIT Press, Cambridge. See also: Hodgins, J., Raibert, M. H. 1990. Biped gymnastics, International J. Robotics Research, 9:(2) 115-132 (https://www.researchgate.net/publication/220122476_Biped_Gymnastics).

This article explores the implications of one type of cognitive technology, techniques and concepts to develop joint human-machine cognitive systems, for the application of computational technology by examining the joint cognitive system implicit in a hypothetical computer consultant that outputs some form of problem solution. This analysis reveals some of the problems can occur in cognitive system design-e.g., machine control of the interaction, the danger of a responsibility-authority double-bind, and the potentially difficult and unsupported task of filtering poor machine solutions. The result is a challenge for applied cognitive psychology to provide models, data, and techniques to help designers build an effective combination between the human and machine elements of a joint cognitive system.

It is certainly important that this turns Another purpose of the book might be to serve as out to be the correct pairing of adjectives with nouns.

Many of the ideas behind object-oriented programming have roots going back to SIMULA. The first substantial interactive, display-based implementation was the SMALLTALK language. The object-oriented style has often been advocated for simulation programs, systems programming, graphics, and AI programming. The history of ideas has some additional threads including work on message passing as in ACTORS, and multiple inheritance as in FLAVORS. It is also related to a line of work in AI on the theory of frames and their implementation in knowledge representation languages such as KRL, KEE, FRL, and UNITS.

One day a man, who had lost Using an ordinary text-editing algorithm and a variety of much of his long-term episodic memory, consulted the professor changeable key words, the man could call up stories on his to ask him if there was any way he could help him personal computer, read them aloud, and thus attempt to regain the lost memories. Being righthanded text-editing method is trivial, but this is not an article and left-hemisphere specialized for language, he about method; it is about ethics.) The hope was that was still able to speak, to read and write: and to understand not only would the man now have some memory to think what was said to him. Besides the usual difficulty about and talk about but, more importantly, this repeated in recalling proper names, his main problem involved large daily practice at his own pace, with no one looking over gaps in his memory for events that he participated in before his shoulder, might help open up new access paths to his the stroke, although he could remember events that own memory of these events, filling them in and modifying occurred after the stroke. He could not, however, remember the award out the plan.

ECAI, Pisa 1984) to facilitate the decompiling of wealth and power.

During the balmy summer of 1980, about 800 AI researchers pose of the new format, the Committee's recommendation, met on the Stanford campus to hold the first and some expanded ways for members to participate in the AAAI conference. The conference program had no more conference this year. For many of Conference Goals those attendees, it was a special, unique opportunity to have deep colleagial interactions in a very comfortable setting. The most radical change that was considered, but not adopted, was the division of the science and engineering interests into two separate conferences at different times of Even the first national conference, however, was more the year. Many Council members expressed concern that than a gathering of researchers.

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.