Collective Collaboration of Recursive Autonomous Agents

Abstract:Smart factories must translate order-level objectives into production tasks at multiple scales and combine the capabilities of individual units into an overall delivery capability. To address repeated adaptation of organizational mechanisms across equipment, workshops, and factories, as well as coordination across levels under changing production conditions, this paper develops a framework for the collective collaboration of recursive autonomous agents (RAAs). A common task and capability interface supports the recursive decomposition of production objectives from the top down and the aggregation of conditional capabilities through peer collaboration from the bottom up. Composite units can therefore participate in higher-level organizations as agents of the same kind. We formulate organizational models for leaf and composite units, specify interface closure under composition and conditions for finite task expansion, and develop operational mechanisms linking task decomposition, resource commitments, result aggregation, and coordination within the scope affected by change. A production network integrating machining, inspection, and logistics across multiple workshops illustrates how a common organizational kernel is instantiated at the equipment-service, workshop, and factory scales. The framework connects recursive organization with explicit task responsibilities, capability conditions, and execution interfaces for manufacturing systems.

Machine Learning (cs.LG)

arXiv:2610.33515 [cs.LG]

(or arXiv:2610.33515v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.33515 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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