Enhancing XCS with Dual-Stream Identification for Perceptual Aliasing in Multi-Step Decision-Making
Fumito Uwano, Will Neil Browne · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
Perceptual aliasing, where distinct states appear indistinguishable due to sensor limitations or environmental ambiguities, poses significant challenges in multi-step decision-making. The eXtended Classifier System (XCS) addresses this issue by identifying unique state transition patterns and combining them to construct accurate policies. Additionally, state-action-state chains enhance XCS's ability to handle sequentially aliased states. However, XCS processes aliased states sequentially as they are perceived, which can lead to performance degradation when incorrect versions of aliased states are included in the chain. This limitation underscores the need for a more robust mechanism to accurately differentiate unique states from aliased ones to ensure reliable policy creation. To address this, we propose a dual-stream identification framework that enhances XCS's performance in environments with perceptual aliasing. The framework introduces two parallel identification processes: one captures immediate state-action relationships, while the other identifies broader patterns across multi-step sequences. By integrating these dual streams, the proposed approach effectively disambiguates aliased states, enabling more accurate decision-making. Experimental evaluations demonstrate that our dual-stream model outperforms state-of-the-art XCS implementations across 14 benchmark environments.