Repairing Learned Knowledge Using Experience

Explanation-based learning occurs when something useful is retained from an explanation, usually an account of how some particular problem can be solved given a sound theory. Many real-world explanations are not based on sound theory, however, and wrong things may be learned accidentally, as subsequent failures will likely demonstrate. In this paper, we describe ways to isolate the facts that cause failures, ways to explain why those facts cause problems, and ways to repair learning mistakes. In particular, our program learns to distinguish pails from cups after making a few mistakes.

Calculation of Blocking Probabilities in Multistage Interconnection Networks with Redundant Paths

The blocking probability of a network is a common measure of its performance. There exist means of quickly calculating the blocking probabilities of Banyan networks; however, because Banyan networks have no redundant paths, they are not inherently fault-tolerant, and so their use in large-scale multiprocessors is problematic. Unfortunately, the addition of multiple paths between message sources and sinks in a network complicates the calculation of blocking probabilities. A methodology for exact calculation of blocking probabilities for small networks with redundant paths is presented here, with some discussion of its potential use in approximating blocking probabilities for large networks with redundant paths.

A Lifetime-based Garbage Collector for LISP Systems on General-Purpose Computers

Garbage collector performance in LISP systems on custom hardware has been substantially improved by the adoption of lifetime-based garbage collection techniques. To date, however, successful lifetime-based garbage collectors have required special-purpose hardware, or at least privileged access to data structures maintained by the virtual memory system. I present here a lifetime-based garbage collector requiring no special-purpose hardware or virtual memory system support, and discuss its performance.

Probabilistic Analysis of Multistage Interconnection Network Performance

We present methods of calculating the value of two performance parameters for multipath, multistage interconnection networks: the normalized throughput and the probability of successful message transmission. We develop a set of exact equations for the loading probability mass functions of network channels and a program for solving them exactly. We also develop a Monte Carlo method for approxmiate solution of the equations, and show that the resulting approximation method will always calculate the values of the performance parameters more quickly than direct simulation.

Learning Structural Descriptions from Examples

The research here described centers on how a machine can recognize concepts and learn concepts to be recognized. Explanations are found in computer programs that build and manipulate abstract descriptions of scenes such as those children construct from toy blocks. One program uses sample scenes to create models of simple configurations like the three-brick arch. Another uses the resulting models in making identifications. Throughout emphasis is given to the importance of using good descriptions when exploring how machines can come to perceive and understand the visual environment.

New Progress in Artificial Intelligence

This report concentrates on progress during the last two years at the M.I.T. Artificial Intelligence Laboratory. Topics covered include the representation of knowledge, understanding English, learning and debugging, understanding vision and productivity technology. It is stressed that these various areas are tied closely together through certain fundamental issues and problems.

Progress in Vision and Robotics

The Vision Flashes are informal working papers intended primarily to stimulate internal interaction among participants in the A.I. Laboratory's Vision and Robotics group. Many of them report highly tentative conclusions or incomplete work. Others deal with highly detailed accounts of local equipment and programs that lack general interest. Still others are of great importance, but lack the polish and elaborate attention to proper referencing that characterizes the more formal literature. Nevertheless, the Vision Flashes collectively represent the only documentation of an important fraction of the work done in machine vision and robotics. The purpose of this report is to make the findings more readily available, but since they are not revised as presented here, readers should keep in mind the original purpose of the papers!