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This paper proposes integrating Promise Theory with Bayesian probability and Active Inference to address coordination and normalization challenges in autonomous agent systems. By framing boundary conditions as semantic promises, the approach offers a scalable definition of intent that complements traditional probabilistic methods. The work targets researchers in multiagent systems and machine learning seeking robust frameworks for swarm intelligence and agent alignment.
Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
arXiv:2606.08552v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Neural and Evolutionary Computing (cs.NE); Data Analysis, Statistics and Probability (physics.data-an)
I.2.11; F.4.1; I.2.4; G.2.2; G.3; I.6
arXiv:2606.08552 [cs.AI]
(or arXiv:2606.08552v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.08552 arXiv-issued DOI via DataCite (pending registration)
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