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XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making

Fumito Uwano & Prof Will Browne

Evolutionary Computation2026https://doi.org/10.1162/evco.a.392article
AJG 3
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1162/evco.a.392

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@article{fumito2026,
  title        = {{XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making}},
  author       = {Fumito Uwano & Prof Will Browne},
  journal      = {Evolutionary Computation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1162/evco.a.392},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.