Adaptive Self-Directed Intelligence Framework for Hyperconnected Autonomous Systems
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Updated time：2026-07-27 13:16:28 Views：12
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Abstract
Abstract—Since 6G will entail hyperconnected autonomous systems, ranging from edge devices and robotic car fleets to Things gateways, such systems need to autonomously adjust their decision policies without waiting for a central controller to intervene. While centralized cloud intelligence, federated learning aggregation and classic autonomous intelligent systems are the current paradigms, all of these have drawbacks: either too much intelligence resides on the coordinator or is voted for the aggregation without taking into account both the per-agent confidence and the intelligence itself, or fixed learning rates are used and are not suitable for hyperconnected traffic with varying volatility. In this paper, the concept of Adaptive Self-Directed Intelligence Framework (ASDIF) is proposed, where the agents continuously model themselves with an almost updated self-model of how competent they are, arbitrate their own goals with confidence that is mutually community-supported via an almost hyperconnectivity bus, and adapt their individual learning rates to the observed degradation of their ability. ASDIF is defined by seven original equations: self-model confidence, hyperconnection trust weighting, autonomy arbitration adaptive learning rate, self-directed goal selection, energy cost and a compound adaptability index. An existing autonomous-intelligent-system baseline, centralized intelligence, and federated learning-based coordination were compared to ASDIF using a discrete-event simulator at hyperconnectivity densities ranging from 2-15 links per node. AS-DIF increased accuracy by up to 27.4 percentage points at high density compared to centralized intelligence, reduced average latency by 58%, and improved the resilience score from 0.50 to 0.93. These results indicate that combining self-directed confidence with hyperconnected trust exchange gives more flexible autonomy, more energy-efficient than current paradigms.
Keywords
self-directed intelligence; hyperconnected systems; adaptive autonomy; trust-aware coordination; distributed intelligence; 6G edge computing; autonomous decision-making.
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