XCS for Sequential Perceptual Aliasing in Multi-Step Decision-Making
针对机器人因顺序感知混淆导致决策困难的问题，提出一种基于分层参考框架的XCS系统（Hi-FoRsXCS），通过构建混淆状态链来学习完整动作映射，实验表明其准确率优于现有系统。
Sequential perceptual aliasing is a cognitive challenge for learning agents when robots cannot differentiate states and their associations based on immediate observations, leading to poor decision-making. Existing systems struggle to abstract and distinguish observations effectively to achieve policy learning. This paper addresses this issue by introducing new aliasing types within the context of sequential aliasing and proposing an enhanced XCS classifier system that learns using a complete state-action map. The proposed system called hierarchical Frames-of-References-based XCS (Hi-FoRsXCS), can concatenate sequences of aliased states with the same observation into a chain. Hi- FoRsXCS then predicts associations between the observations and aliased states using the ends of the chain, enabling optimal policy learning with a complete action map. Experimental results demonstrate that Hi-FoRsXCS outperforms the existing systems in terms of accuracy. However, the limitations of Hi-FoRsXCS will be discussed in this paper.