[Paper] Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
Source: arXiv - 2606.08552v1
Overview
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.
Key Contributions
This paper presents research in the following areas:
- cs.AI
- cs.MA
- cs.NE
- physics.data-an
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.AI.
Authors
- Mark Burgess
Paper Information
- arXiv ID: 2606.08552v1
- Categories: cs.AI, cs.MA, cs.NE, physics.data-an
- Published: June 7, 2026
- PDF: Download PDF