Brown, J. S.

A qualitative physics based on confluences


A qualitative physics predicts and explains the behavior of mechanisms in qualitative terms. The goals for the qualitative physics are (1) to be far simpler than the classical physics and yet retain all the important distinctions (e.g., state, oscillation, gain, momentum) without invoking the mathematics of continuously varying quantities and differential equations, (2) to produce causal accounts of physical mechanisms that are easy to understand, and (3) to provide the foundations for commonsense models for the next generation of expert systems. This paper presents a fairly encompassing account of qualitative physics. Second, we present a framework for modeling the generic behavior of individual components of a device based on the notions of qualitative differential equations (confluences) and qualitative state.

Why AM and EURISKO appear to work


The am program was constructed by Lenat in 1975 as an early experiment in getting machines to learn by discovery. In the preceding article in this issue of the AI Journal, Ritchie and Hanna focus on that work as they raise several fundamental questions about the methodology of artificial intelligence research. Their considerations, and our post-am work on machines that learn, have clarified why am succeeded in the first place, and why it was so difficult to use the same paradigm to discover new heuristics. Some criticism of the paradigm of this work arises due to the ad hoc nature of many pieces of the work; at the end of this article we examine how this very adhocracy may be a potential source of power in itself.