AI Learning and Conceptual Transfer in the Game of Hidden Rules

Abstract:This report summarizes the work conducted on the Game of Hidden Rules (GOHR), focusing on reinforcement learning agents trained to infer hidden rules from trial-and-error feedback, representation design, rule difficulty analysis, transfer learning, generalization, and pseudo-bot-assisted human learning analysis. The report focuses on the Transformer-based A2C framework, Feature-Centric and Object-Centric representations, experimental findings, and classification of human learning data.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2608.21372 [cs.AI]

(or arXiv:2608.21372v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.21372 Focus to learn more arXiv-issued DOI via DataCite

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