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

Fields 3-8 of table 1 of the survey and general results, a discussion represent purposes, specifically, to define of the four hypotheses, and two sections models (field 3), prove theorems about the at the end of the article that contain details of models (field 4), present algorithms (field 5), the survey and statistical analyses. The next analyze algorithms (field 6), present systems section (The Survey) briefly describes the 16 or architectures (field 7), and analyze them substantive questions I asked about each (field 8). These purposes are not mutually paper. One of the closing sections (An Explanation exclusive; for example, many papers that of the Fields in Table 1) discusses the present models also prove theorems about criteria for answering the survey questions the models.

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

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.

This article reports on experiments performed using a black-box simulation of a spacecraft. The goal of this research is to learn to control the attitude of an orbiting satellite. The space-craft must be able to operate with minimal human supervision. To this end, we are investigating the possibility of using adaptive controllers for such tasks. Laboratory tests have suggested that rule-based methods can be more robust than systems developed using traditional control theory. The BOXES learning system, which has already met with success in simulated laboratory tasks, is an effective design framework for this new exercise.

Advocates and critics of AI have long engaged in a debate that has generated a great deal of heat but little light. Whatever the merits of specific contributions to this ongoing debate, the fact that it continues points to the need for a reflective examination of the foundations of AI by its active practitioners. Following the lead of Earl MacCormac, we hope to advance such a reflective examination by considering questions of metaphor in science and the computational metaphor in AI. Specifically, we address three issues: the role of metaphor in science and AI, an examination of the computational metaphor, and an introduction to the possibility and potential value of using alternative metaphors as a foundation for AI theory.

Dr. Northrup Fowler III Rome Laboratory Recently I circulated the Waltz taxonomy MVL theorem proving taxonomy, I wonder if AAAI system available by anonymous ftp might not consider a broader review from Stanford. Systems architectures and thereby gain some sense 2. Loop detection and recursion control of current relative interest and, over in the underlying theorem prover. Featuring applications in: of the discipline as a whole relative 4. A fast unifier that includes an Banking and Finance a valuable service to those who serve sequence variables. Published by I'm surprised in a way that AAAI t.stanford.edu, AAAI Press hasn't already undertaken this effort, "anonymous" as your user name, followed as do other professional organizations by any password you wish.

The VLSI cell placement problem is known to be NP-complete. This paper presents a survey of the various approaches and techniques for this problem. It also gives a comprehensive tutorial on the subject, providing an excellent introduction to the terminology and classification of placement algorithms. With the growing diversity of the terms appearing in the literature, I found the explicit warning about synonymous usage of words like module, cell, and element or net, wire, and interconnect to be helpful. The placement algorithms whose emphasis is on standard cell and macro placement fall into five groups, according to their underlying technique: (1) simulated annealing, (2) force-directed, (3) minimum-cut, (4) numerical optimization, and (5) evolution based. The origins of the first two are in physical laws.

One of the most crucial problems in any computer system that involves representing the world is the representation of time. This includes applications such as databases, simulation, expert systems, and applications of Artificial Intelligence in general. In this brief article, I will give a survey of the basic techniques available for representing time, and then talk about temporal reasoning in a general setting as needed in AI applications. Quite different representations of time are usable depending on the assumptions that can be made about the temporal information to be represented. Can one assume that a timestamp can be assigned to each event, or barring that, that the events are fully ordered?