\documentstyle{article} \begin{document} \title{A SYSTEM FOR
INCREMENTAL LEARNING BASED ON ALGORITHMIC PROBABILITY}
\author{Ray J. Solomonoff \thanks{Proceedings of the Sixth
Israeli Conference on Artificial Intelligence, Computer Vision
and Pattern Recognition, Dec. 1989}\\ Computer Learning Research
Center\\ Royal Holloway, University of London\\ Mailing Address:
P.O.B. 400404, Cambridge, Ma. 02140, U.S.A.\\ Email:
rjsolo@ieee.org} \date{Dec. 1989} \maketitle \begin{abstract} 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
efficiency with which they learn --- both in computation time
needed and/or in amount of data needed for learning. Algorithmic
Probability Theory provides a general