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Adaptive Self-Directed Intelligence Framework for Hyperconnected Autonomous Systems
编号：158 访问权限：仅限参会人 更新：2026-07-27 13:16:28 浏览：18次 Online

报告开始：2026年08月01日 10:00（Asia/Kolkata）

报告时间：15min

所在会场：[S8] Mixed Track Session [S8-1] Mixed Track Session

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摘要
                         

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.
 
关键词
self-directed intelligence; hyperconnected systems; adaptive autonomy; trust-aware coordination; distributed intelligence; 6G edge computing; autonomous decision-making.
报告人
Bhavani p
ASSISTANT PROFESSOR Trichy；K.Ramakrishnan College of Engineering

稿件作者
Arti Badhoutiya GLA University； Mathura
Venkateshwar Rao .B. CMR College of Engineering & Technology, Hyderabad, Telangana, India
Anish Gupta School of Engineering and Technology (SET)； CGC University； Mohali
NAVEENKRISHNA N Shri Venkateshwara Padmavathy Engineering College
DANIEL DAS A Karpagam Academy of Higher Education Coimbatore
Bhavani p Trichy；K.Ramakrishnan College of Engineering
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重要日期

  • 会议日期

    07月30日

    2026

    至

    08月01日

    2026

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    初稿截稿日期

  • 08月03日 2026

    注册截止日期

主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
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