用承诺理论量化智能体意图，解决自主系统推理中的概率困境
Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
- 将承诺理论与贝叶斯推断、主动推理结合，构建可计算的意图表达
- 通过边界条件约束状态空间，实现决策阈值的自适应选择
- 适合研究自主系统、群体智能与跨系统对齐的学者
本文探讨了承诺理论在涉及自主智能体过程中的定量表示。智能体模型广泛应用于软件系统、机器学习和生物学，也可能适用于物理学及其他工程领域。文中说明如何将贝叶斯概率与信息论优化（包括主动推理）融入承诺语义；同时指出承诺理论可补充现有方法，避免概率计算中的非局部协调、校准及归一化等难题。边界条件在限制允许状态和选择决策阈值方面体现为一种承诺，而智能体对齐提供了可扩展的意图定义。尽管存在不确定性会促使信息最大化，自主智能体仍可通过最小化自身信息来形成具有超智能体特性的群体。该方法虽带来研究挑战与风格偏好，但为复杂系统建模提供了新视角。
原文摘要 · Abstract (English)
I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, machine learning, and biology, for example, but may also apply to physics and other forms of engineering. I describe how Bayesian probability and information theoretic optimization, including Active Inference, may be incorporated with promise semantics -- as well as how Promise Theory supplements solutions, helping to avoid probability's pitfalls, which include non-local coordination, calibrating, and normalizing probabilistic computations. The role of boundary conditions in constraining allowed states and selecting decision thresholds is a form of promise, and agent alignment provides a scalable definition of intent. Autonomous agents may congeal into swarms with superagent characteristics by trying to minimize their information, despite uncertainty that works to maximize it. The use of Promise Theory involves some research challenges as well as stylistic preferences.
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