Distributed Artificial Intelligence for Agentic Intent-Based Networking in Mobile Networks

Ioannou, I, Naegele-Jackson, S, Filiposka, S, Burkard, V, Georgiades, M, Mackay, M ORCID: 0000-0001-9013-7884 and Vassiliou, V (2026) Distributed Artificial Intelligence for Agentic Intent-Based Networking in Mobile Networks. Future Internet, 18 (10). ISSN 1999-5903

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Abstract

Through intent-based networking (IBN), network objectives are specified declaratively, yet in mobile networks their realisation spans administrative domains that compete for coupled radio, transport and compute resources but cannot share local models and telemetry. A distributed artificial intelligence (DAI) controller with a Belief-Desire-Intention-eXecution (BDIX) cycle is presented, centred on shared-space coordination. The local decision of each domain agent is mapped through (Formula presented.) into a common p-dimensional resource space, so that only resource contributions and a common price vector cross domains, after a minimum-slack feasibility certificate and behind a trust-admission gate. Typed refinement, deficit feedback and conflict mediation support it. In a coordination-layer benchmark (4 to 64 domains, twenty seeds), the full-information objective is matched to the reported numerical precision without disclosure of local models (cost gap 0.00%), whereas, at the principal (Formula presented.) setting, aggregate-only allocation loses 2.27% to 3.83% and equal split loses 25.21% to 37.67%. When 12.50% to 25.00% of the domains understate their contributions fivefold, honest-domain cost rises by 24.48% to 50.95% without the gate and by at most 2.26% with it, while mild falsification is not reliably detected by the trust gate, leaving containment dependent on the delegated resource bounds. On the single-domain analytical plant (twenty hold-out seeds), the controller attains the joint-highest displayed score (0.80), the lowest mean latency and significantly higher scores than seven of eleven comparators; end-to-end workflow execution is demonstrated separately by bridge dry runs. Shared-space coordination is thus the allocation of choice where local models cannot be shared.

Item Type:	Article
Uncontrolled Keywords:	4606 Distributed Computing and Systems Software; 46 Information and Computing Sciences; Machine Learning and Artificial Intelligence; 46 Information and computing sciences
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Divisions:	Computer Science and Mathematics
Publisher:	MDPI AG
Date of acceptance:	28 September 2026
Date of first compliant Open Access:	4 November 2026
Date Deposited:	04 Nov 2026 13:54
Last Modified:	04 Nov 2026 13:54
DOI or ID number:	10.3390/fi18100530
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29816

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