A SYSTEM FOR INCREMENTAL LEARNING BASED ON ALGORITHMIC PROBABILITY
Ray J. Solomonofi · 1989
We have employed Algorithmic Probability Theory to construct a system for machine learning of great power and generality. The principal thrust of present research is the design of sequences of problems to train this system. Current programs for machine learning are limited in the kinds of concepts accessible to them, the kinds of problems they can learn to solve, and in the e‐ciency with which they learn | both in computation time needed and/or in amount of data needed for learning. Algorithmic Probability Theory provides a general model of the learning process that enables us to understand and surpass many of these limitations. Starting with a machine containing a small set of concepts, we use a carefully designed sequence of problems of increasing di‐culty to bring the machine to a high level of problem solving skill. The use of training sequences of problems for machine knowledge acquisition promises to yield Expert Systems that will be easier to train and free of the brittleness that characterizes the narrow specialization of present day systems of this sort. It is also expected that the present research will give needed insight in the design of training sequences for human learning.