Paper · arXiv
AI Learning and Conceptual Transfer in the Game of Hidden Rules
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.
| Authors | Christo Mathew, Wentian Wang, Jacob Feldman et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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