Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world application related to the problem of navigation. However, the best achievements of Learning Classifier Systems (LCS) in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. Also, despite the fact that the maze environment problem has a long history of usage in research into learning, there has been little analysis on the complexity of maze problems. To overcome these restrictions we try to improve our understanding of the nature and structure of maze environments. We analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics and develop a set of new maze environments. We then construct a new LCS agent so that it has a simpler and more transparent performance mechanism, and still could solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well, and in some cases better than other published results. Running Head: Learning Mazes with Aliasing States