Presentation Details
Adaptive Self-Directed Intelligence Framework for Hyperconnected Autonomous Systems
ID：158
View Protection：ATTENDEE
Updated Time：2026-07-27 13:16:28 Hits：7
Online
Start Time：2026-08-01 10:00（Asia/Kolkata）
Duration：15min
Session：S8 Mixed Track Session »
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.
Speaker
Bhavani p
ASSISTANT PROFESSOR Trichy；K.Ramakrishnan College of EngineeringSubmission Author
All Comments
Important Date
-
Conference Date
Jul 30
2026
toAug 01
2026
-
Jul 26 2026
Draft paper submission deadline
-
Jul 28 2026
Registration deadline
Sponsored By
The United Societies of Science
Organized By
Kongunadu College of Engineering and Technology
Supported By
IEEE Section
IEEE Madras Section
IEEE Madras Section
Contact Information
Previous Conferences
-
2026-07-30 India Trichy
2026 International Conference on Networks Computers and Communications
Submit Comment