Cooperation in Human and Machine Agents: Promise Theory Considerations

TL;DR

This paper explores cooperation in human and machine agents through Promise Theory, providing a unified framework for understanding organization, trust, and interaction in autonomous systems.

Contribution

It applies Promise Theory to analyze cooperation among humans, machines, and AI, offering new insights into agent signaling, trust, and system success.

Findings

Promise Theory models signaling, trust, and feedback between agents.

Revisits principles of agent cooperation across humans and machines.

Provides lessons on success and failure in autonomous agent systems.

Abstract

Agent based systems are more common than we may think. A Promise Theory perspective on cooperation, in systems of human-machine agents, offers a unified perspective on organization and functional design with semi-automated efforts, in terms of the abstract properties of autonomous agents, This applies to human efforts, hardware systems, software, and artificial intelligence, with and without management. One may ask how does a reasoning system of components keep to an intended purpose? As the agent paradigm is now being revived, in connection with artificial intelligence agents, I revisit established principles of agent cooperation, as applied to humans, machines, and their mutual interactions. Promise Theory represents the fundamentals of signalling, comprehension, trust, risk, and feedback between agents, and offers some lessons about success and failure.