In AAAI-86, Vol. 1, pp. 210–214.

Artificial Intelligence, 28:127-162

Artificial Intelligence, 28 (2), 225-33.

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.

MIT Press.

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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).

Review of Readings in Medical Artificial Intelligence: The First Decade

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. 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.

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.