AutCn

AutCn is an acronym used in discussions of distributed, self-managing, learning-enabled computer networks. The term is not standardized; common expansions include Autonomous Cognitive Network, Autonomous Control Network, and Automated Cognitive Network. In its broad conception, AutCn refers to a class of networked systems composed of autonomous agents that can perceive, reason, learn, and act to achieve goals with limited or no centralized control.

An AutCn typically combines a cognitive layer that handles perception, inference, and learning with an autonomous control layer that executes decisions and adapts to changing conditions. A governance or policy layer often governs safety, privacy, and interoperability. Data flows support feedback loops: perception leads to decision, which updates models and configurations.

Applications include dynamic network management in data centers and software-defined networks, coordination of Internet of Things devices, and optimization of energy usage in smart grids. Research questions focus on autonomy reliability, scalable learning, interoperability standards, and secure agent coordination.

Challenges include ensuring safety and controllability, protecting privacy, avoiding cascading failures, and achieving explainability of agent decisions. Adoption faces questions about governance, accountability, and compatibility with legacy systems.

See also: Autonomous system, Multi-agent system, Distributed AI, Edge computing, AI governance.