Evolvable Multi-Agent Hierarchical Architecture for Contested Logistics Ecosystems
Figure
5-3: AI-Ready Data Intelligence Platform architecture.
Core platform capabilities include: semantic alignment of heterogeneous data; automated data quality assessment; provenance and trust metadata preservation; support for real-time and batch analytics; and governance for secure cross-organisational data sharing.[ 9 ][ 10 ]
For contested environments, the platform additionally implements zero-trust security principles, [ 14 ] providing: agent authentication and identity verification to prevent impersonation and unauthorised participation; cross-source contradiction analysis to surface conflicts between independently sourced data indicative of deception; anomaly detection for data patterns inconsistent with established agent behaviour; adversarial validation [ 13 ] flagging data that passes formatting and provenance checks but deviates anomalously from expected ranges; and trust scoring that evolves continuously with historical agent and participant performance, rather than treating prior authorisation as sufficient.
These capabilities distinguish between missing data( addressed through confidence scoring), erroneous data( addressed through quality checks), and potentially malicious data( addressed through adversarial validation and behavioural anomaly detection). By decoupling data intelligence from individual applications or agents, [ 11 ] the platform enables reasoning about both uncertainty and adversarial behaviour, creating conditions for genuinely trust-aware decisionmaking across complex, multi-party logistics ecosystems.
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