用熵势衡量事件对未来的不确定性影响，提升AI决策与解释性。
Extending the Entropic Potential of Events for Uncertainty Quantification and Decision-Making in Artificial Intelligence
- 以事件为中心定义熵势，量化离散事件对未来熵的影响。
- 在强化学习与异常检测中验证其有效提升不确定性建模能力。
- 适合关注可解释性与鲁棒决策的AI研究者使用。
本文展示如何将事件的熵势这一概念——一个衡量离散事件对系统未来期望熵影响的参数——应用于人工智能中的不确定性量化、决策与可解释性。该框架源自物理学原初定义，经调整后适用于AI，引入以事件为核心的度量，捕捉动作、观测等离散事件对未来时间窗内不确定性的扰动。同时形式化了原始与适配后的熵势定义，后者强调条件期望以处理反事实情景。应用涵盖策略评估、内在奖励设计、可解释AI与异常检测，凸显该度量统一并强化智能系统中不确定性建模的潜力。概念示例涉及强化学习、贝叶斯推断与异常检测，亦讨论复杂模型中计算的实践考量。熵势框架为管理AI不确定性提供了理论扎实、可解释且通用的方法，连接热力学、信息论与机器学习原理。
原文摘要 · Abstract (English)
This work demonstrates how the concept of the entropic potential of events -- a parameter quantifying the influence of discrete events on the expected future entropy of a system -- can enhance uncertainty quantification, decision-making, and interpretability in artificial intelligence (AI). Building on its original formulation in physics, the framework is adapted for AI by introducing an event-centric measure that captures how actions, observations, or other discrete occurrences impact uncertainty at future time horizons. Both the original and AI-adjusted definitions of entropic potential are formalized, with the latter emphasizing conditional expectations to account for counterfactual scenarios. Applications are explored in policy evaluation, intrinsic reward design, explainable AI, and anomaly detection, highlighting the metric's potential to unify and strengthen uncertainty modeling in intelligent systems. Conceptual examples illustrate its use in reinforcement learning, Bayesian inference, and anomaly detection, while practical considerations for computation in complex AI models are discussed. The entropic potential framework offers a theoretically grounded, interpretable, and versatile approach to managing uncertainty in AI, bridging principles from thermodynamics, information theory, and machine learning.
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