Mark E. Lacy is manager of computational texts will be needed, and hopefully,

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.

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.

Artificial Intelligence 51, pp. 95-143

Abstraction, in contrast to meta-level inference, works with a degenerate version of the object-level space in which some essential detail is thrown away. Because abstract plans are strongly tied to the object-level space, they are limited in their expressive power.

Artificial Intelligence 51 (1991) 417-471, Spatial reasoning is ubiquitous in human problem solving. Significantly, many aspects of it appear to be qualitative. This paper describes a general framework for qualitative spatial reasoning and demonstrates how it can be used to understand complex mechanical systems, such as clocks. The framework is organized around three ideas.

This is similar to the work of McDermott [31].

In Lifschitz, V. (Ed.), Artificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John McCarthy, pp. 359–380. Academic Press.

To cite or link to this reference: http://hdl.handle.net/10068/43975

In Daniel G. Bobrow and Alan Model of Computation Based on a Calculus University of New York at Albany, 1986. On the of many sorted interpolation theorems. An investigation [Höhfeld and Smolka, 1988] Markus Höhfeld in Expert Systems III, pages 184-194, into inference with restricted and G. Smolka. A many-sorted resolution based Levesque, and Raymond Reiter, editors, 2(3):142-150, 1986. An overview in a topically organized semantic of the HORNE logic programming system.