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.

Partial Evaluation for Scientific Computing: The Supercomputer Toolkit Experience

We describe the key role played by partial evaluation in the Supercomputer Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputer Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at M.I.T., and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable.