This symposium was very successful and was perhaps the most unusual of the spring symposia this year. It brought together for the first time distinguished researchers from many diverse disciplines to discuss and share results on a particular topic of mutual interest. The disciplines included machine learning, computational learning theory, computer vision, pattern recognition, perceptual psychology, statistics, information theory, theoretical computer science, and molecular biology, with the involvement of the latter group having lead to a joint session with the AI and Molecular Biology symposium.

A workshop on defeasible reasoning with specificity was held under the arch in St. Louis during April 1989, with support from AAAI and McDonnell Douglas, and the assistance of Rockwell Science Center Palo Alto and the Department of Computer Science of Washington University.

As you can see from direct inspection of the magazine, it's a little different I hope you enjoy this special issue.

Truth maintenance is a collection of techniques for doing belief revision. A truth maintenance system's task is to maintain a set of beliefs in such a way that they are not known to be contradictory and no belief is kept without a reason. Truth maintenance systems were introduced in the late seventies by Jon Doyle and in the last five years there has been an explosion of interest in this kind of systems. In this paper we present an annotated bibliography to the literature of truth maintenance systems, grouping the works referenced according to several classifications.

Clancey troubleshooting tutor for only 20 The cognitive modeling group provided would like to see alternative cognitive hours gained a proficiency equivalent strong advocacy for the use of models available within a system to that of trainees with 40 months cognitive modeling in building these rather than a single "correct" model (almost 4 years) on-the-job training systems. They argued for increased used to justify instruction.

Molecular biology is emerging as an important domain for artificial intelligence research. The advantages of biology for design and testing of AI systems include large amounts of available online data, significant (but incomplete) background knowledge, a wide variety of problems commensurate with AI technologies, clear standards of success, cooperative domain experts, non-military basic research support and percieved potential for practical (and profitable) applications. These considerations have motivated a growing group of researchers to pursue both basic and applied AI work in the domain. More than seventy-five researchers working on these problems gathered at Stanford for a AAAI sponsored symposium on the topic. This article provides a description of much of the work presented at the meeting, and fills in the basic biology background necessary to place it in context.

There is a great disparity between the number of papers which have been published about AI-based manufacturing scheduling tools and the number of systems which are in daily use by manufacturing engineers. It is argued that this is not a reflection of inadequate AI technology, but is rather indicative of lack of a systems perspective by AI practitioners and their manufacturing customers. Case studies to support this perspective are presented by Carnegie Group as a builder of scheduling systems for its customers, by Texas Instruments and Intel Corporation as builders of schedulers for their own use, and by Intellection as a consulting house specializing in scheduling problems.

Furthemore, sequence similarity often results from common ancestors. Immunoglobulin (Ig) domains are sets of,a-sheets bound 424 Bengio, Bengio, Pouliot and Agin by cysteine bonds and with a characteristic tertiary structure. Such domains are found in many proteins involved in immune, cell adhesion and receptor functions. These proteins collectively form the immunoglobulin superfamily (for review, see Williams and Barclay, 1987). Members of the superfamily often possess several Ig domains.

The distributed-neuron synapses are arranged inblocks of 16, which we call '4 x 4 tiles'. Switch matrices are interleaved between each of these tiles to provide programmability ofinterconnections. With a small area overhead (15 %), the 1024 units of the network can be rearranged in various configurations. Someof the possible configurations are, a 12-32-12 network, a 16-12-12-16 network, two 12-32 networks etc. (the numbers separated bydashes indicate the number of units per layer, including the input layer). Weights are stored in analog form on MaS capacitors.

These algorithms are also related to time-independent fonnalisms.