Adaptive Entangled Game Modules in Artificial General Intelligence
Read the original on arXiv AI →The paper presents a probability‑wave framework for modeling the collective behavior of adaptive agents, deriving testable eigenmodes via a generalized behavioral intelligence (GBI) nonlocal probability‑wave equation. Empirical analysis of Chinese intraday stock market data shows that adaptive entangled game modes explain 89% of observed decision patterns, far exceeding predictions from neoclassical finance and supporting the Liu‑Chen‑Ao hypothesis of nonlocal entangled nerve fibers. The authors argue that incorporating these adaptive entangled game modules into AGI architectures can overcome limitations of conventional ANN‑based AI and enable more compact, efficient, and robust human‑like processing units for embodied intelligence and robotics.
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