A Maze Solver based on a New Architecture of XCS

Learning capabilities of an agent relies on the way that agent perceives the environment. When the agent’s sensations convey only partial information about the environment, there may be different situations that appear identical to the agent but require different actions to behave optimally. In this paper, we propose a new approach to improve XCS’s performance in Partially Observable Markov Decision Process (POMDP) using a newly introduced method to detect aliased states in the current environment. In our approach, at the initial state, there exists only a single main XCS which handles all of the environmental states. When an existing aliased state is detected using a simple mechanism, the system creates a new XCS, in addition to the main XCS which we call Cooperative XCS. The new XCS is responsible for handling this detected state. This mechanism allows the main XCS to handle non-aliased states and the other XCS’s cooperate with it by handling existing aliased states independently. Thus, the system is called Cooperative Specialized XCS and its performance is compared with some other classifier systems in some benchmark problems. The presented results demonstrate the effectiveness of our proposed approach.

Keywords:

Cooperative XCS , Learning Classifier System , Partially Observable Environments , XCS