🤖 AI Summary
This work addresses key challenges in integrating intention modeling with probabilistic reasoning for autonomous agents—particularly non-local coordination, calibration, and normalization—by proposing a novel paradigm that deeply integrates commitment semantics with active inference. By expressing boundary conditions and decision thresholds as commitments, the approach constrains the state space and guides Bayesian inference alongside information-theoretic optimization, thereby enhancing probabilistic robustness while preserving semantic clarity. Coupled with an agent alignment mechanism, the system enables the emergence of super-agent collective behaviors under uncertainty with minimal informational overhead, effectively alleviating the coordination and computational bottlenecks inherent in conventional probabilistic methods.
📝 Abstract
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.