Evolvable Multi-Agent Hierarchical Architecture for Contested Logistics Ecosystems
autonomy, aligning decision-making authority with strategic, operational, and tactical layers. Local agents act autonomously within defined boundaries while higher-level agents maintain coherence with broader objectives. Hierarchy here provides structure for coordinating distributed decision-making, supporting both top-down intent propagation and bottom-up feedback.
4.3 INTEROPERABILITY BY DESIGN
Modern logistics ecosystems are inherently multi-organisational. Architectures relying on tight coupling or bespoke integrations inhibit collaboration. [ 3 ] The architecture treats open interfaces, shared semantics, and clear governance mechanisms as core concerns. Agents interact through common abstractions, enabling collaboration across boundaries without requiring full system consolidation.
4.4 TRUST, TRANSPARENCY, AND GOVERNANCE OF AUTONOMY
In contested logistics environments, trust is both essential and fragile, and as autonomous capabilities increase, so too does the need for transparency, accountability, and governance.[ 11 ][ 12 ] A critical distinction must be maintained between provenance( knowing where data originated), authenticity( confirming data integrity), reliability( assessing historical accuracy), and trustworthiness( evaluating whether a source is acting in good faith). These are related but distinct properties. Provenance alone is insufficient in adversarial settings: an actor may provide perfectly traceable, correctly formatted, and authenticated data that is nevertheless intentionally misleading. [ 13 ] A supplier may consistently overstate available capacity; a logistics provider may understate expected delays; a compromised sensor network may provide coherent but false telemetry.
The architecture embeds multi-layered trust mechanisms: data is accompanied by provenance and quality metadata; decision processes are structured for traceability and auditability; and human decision-makers are integrated as first-class participants. Beyond data quality assessment, the platform supports cross-source contradiction analysis, anomaly detection for strategic deception patterns, and trust scoring that evolves with historical agent behaviour. Consistent with zero-trust principles, [ 14 ] no participant is implicitly trusted based on identity or prior authorisation alone: trust is continuously earned, verified, and bounded, enabling the system to distinguish degraded from deceptive data.[ 11 ][ 12 ]
4.5 GRACEFUL DEGRADATION UNDER CONTESTED OPERATIONS
Disruption and degradation are normal operating conditions, not edge cases. Architectures optimised for steady-state performance often fail when faced with partial data loss or connectivity degradation. [ 2 ] The architecture supports graceful degradation through distributed agents, local autonomy within bounded decision authorities, and confidence-aware reasoning.
In contested environments, a critical distinction must be drawn between degraded and deceptive scenarios. A disconnected sensor is straightforward to detect: data ceases to flow. A compromised sensor reporting plausible but false information, a pattern well-documented in adversarial machine learning research, [ 13 ] is substantially harder to identify and potentially more
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