  This is the first of a series of papers that the authors propose to write on
the subject of improving the speed of response of learning systems using
multiple models. During the past two decades, the first author has worked on
numerous methods for improving the stability, robustness, and performance of
adaptive systems using multiple models and the other authors have collaborated
with him on some of them. Independently, they have also worked on several
learning methods, and have considerable experience with their advantages and
limitations. In particular, they are well aware that it is common knowledge
that machine learning is in general very slow. Numerous attempts have been made
by researchers to improve the speed of convergence of algorithms in different
contexts. In view of the success of multiple model based methods in improving
the speed of convergence in adaptive systems, the authors believe that the same
approach will also prove fruitful in the domain of learning. In this paper, a
first attempt is made to use multiple models for improving the speed of
response of the simplest learning schemes that have been studied. i.e. Learning
Automata.
