cwa0@gte-labs.CSNET ("Charles W. Anderson") (10/09/86)
The following is the abstract from my Ph.D. dissertation
completed in August, 1986, at the University of Massachusetts, Amherst.
Members of my committee are Andrew Barto, Michael Arbib, Paul Utgoff,
and William Kilmer. I welcome all comments and questions.
Chuck Anderson
GTE Laboratories Inc.
40 Sylvan Road
Waltham, MA 02254
617-466-4157
cwa0@gte-labs
Learning and Problem Solving
with Multilayer Connectionist Systems
The difficulties of learning in multilayered networks of
computational units has limited the use of connectionist systems in
complex domains. This dissertation elucidates the issues of learning in
a network's hidden units, and reviews methods for addressing these
issues that have been developed through the years. Issues of learning
in hidden units are shown to be analogous to learning issues for
multilayer systems employing symbolic representations.
Comparisons of a number of algorithms for learning in hidden
units are made by applying them in a consistent manner to several tasks.
Recently developed algorithms, including Rumelhart, et al.'s, error
back-propagation algorithm and Barto, et al.'s, reinforcement-learning
algorithms, learn the solutions to the tasks much more successfully than
methods of the past. A novel algorithm is examined that combines
aspects of reinforcement learning and a data-directed search for useful
weights, and is shown to out perform reinforcement-learning algorithms.
A connectionist framework for the learning of strategies is
described which combines the error back-propagation algorithm for
learning in hidden units with Sutton's AHC algorithm to learn evaluation
functions and with a reinforcement-learning algorithm to learn search
heuristics. The generality of this hybrid system is demonstrated
through successful applications to a numerical, pole-balancing task and
to the Tower of Hanoi puzzle. Features developed by the hidden units in
solving these tasks are analyzed. Comparisons with other approaches to
each task are made.