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, a
The proliferation of autonomous systems in AI, software, and other fields necessitates robust theoretical frameworks to manage their interactions and intentionality, which Promise Theory aims to provide.
This research offers a foundational approach to understanding and building more reliable and coordinated autonomous agents, addressing critical challenges in scalability, safety, and complex system design.
The proposed integration of Promise Theory with Bayesian probability and active inference could lead to more nuanced and predictable autonomous agent behavior, moving beyond purely probabilistic models.
- · AI researchers
- · Software developers
- · Machine learning engineers
- · Robotics companies
- · Developers of ad-hoc, unprincipled agent systems
- · Systems reliant solely on simpler probability models
More sophisticated and resilient autonomous agent systems emerge, capable of advanced coordination and decision-making.
This improved agent capability accelerates the deployment of AI in critical infrastructure and complex operational environments.
The enhanced reliability of AI agents could lead to higher societal trust and faster integration into daily life and industry, transforming various sectors.
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Read at arXiv cs.AI