  The maze traversal problem (finding the shortest distance to the goal from
any position in a maze) has been an interesting challenge in computational
intelligence. Recent work has shown that the cellular simultaneous recurrent
neural network (CSRN) can solve this problem for simple mazes. This thesis
focuses on exploiting relevant information about the maze to improve learning
and decrease the training time for the CSRN to solve mazes. Appropriate
variables are identified to create useful clusters using relevant information.
The CSRN was next modified to allow for an additional external input. With this
additional input, several methods were tested and results show that clustering
the mazes improves the overall learning of the traversal problem for the CSRN.
