Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents
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 ...
| Authors | Mark Burgess |
|---|---|
| Primary category | cs.AI |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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