Cognitive Learning System for Sequential Aliasing Patterns of States in Multistep Decision-Making

Abstract

Perceptual aliasing is a cognitive problem for a learning agent where the robot cannot distinguish its state via its immediate observations, leading to poor decision-making. Previous work addresses this issue by storing the agent's path to learn the optimal policy. In particular, FoRsXCS utilises a fundamental and unique path to identify and disambiguate all aliased states and learn optimal policies in an environment with aliased states. However, it is hard to identify the aliased states in sequential aliasing patterns of states where the aliased states occur sequentially within a regular pattern. This work proposes a new cognitive learning system to identify such sequential aliasing patterns of states by extending FoRsXCS. The experimental results show that the proposed system performs equal to or greater than the existing systems in nine mazes for navigation tasks and significantly outperforms existing techniques in mazes with sequential aliasing patterns. Concretely, the proposed method improves its performance by 0.48 steps compared with FoRsXCS.