Adaptive Multi‑Agent Defense Against Adversarial Coordination

Draft Patent Application 15 — For Review

Adaptive Multi‑Agent Defense Against Adversarial Coordination

TITLE OF THE INVENTION

Resilient Agentic Coordination Engine for Adaptive Multi‑Agent Defense Against Adversarial Coordination

FIELD OF THE INVENTION

The present invention relates to distributed artificial intelligence, specifically to resilient multi‑agent coordination systems that maintain reliable consensus and interpretability in hostile, dynamic, and uncertain environments such as UAV swarms, cyber‑physical sensor networks, and decentralized finance platforms.

BACKGROUND AND PRIOR ART

Conventional consensus protocols for multi‑agent systems fail to guarantee convergence when even a single agent behaves arbitrarily, as shown by classical impossibility results that bound the number of Byzantine actors and yield only an \((|N|-f,\xi)\) admissible solution with non‑zero residual error ^[v6569]. Recent Byzantine‑resilient algorithms achieve probabilistic or Bayesian robustness, yet they rely on restrictive assumptions or require costly communication overhead ^[v2173], ^[v1592]. Lightweight protocols such as CVT provide sub‑millisecond latency but are limited to threat‑assessment scenarios and do not address dynamic denial‑of‑service or sensor spoofing ^[v46]. Thus, there remains an unmet need for a scalable, formally grounded, and runtime‑explainable multi‑agent defense that guarantees convergence, isolates adversarial influence, and adapts to evolving attack strategies.

SUMMARY OF THE INVENTION

The invention discloses a Resilient Agentic Coordination Engine (RACE) that integrates four complementary innovations—Dynamic Role‑Based Adversarial Training (DRAT), Hybrid Reputation Aggregation (HRA), Trust‑Aware Sensor Fusion with Dynamic Field‑of‑View (TASF‑DFOV), and Randomized Smoothing for LLM‑Based MAS (RS‑LLM‑MAS)—within a three‑layer architecture comprising a world‑model grounding layer, a trust‑aware communication layer, and a dynamic adversarial learning layer. This architecture guarantees Byzantine‑resilient convergence, provides transparent runtime evidence of deviations, and scales sub‑linearly to thousands of agents while maintaining interpretability through ontology‑based justification.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Embodiment 1 – Dynamic Role‑Based Adversarial Training (DRAT)
DRAT pre‑trains agents with a tacit mechanism that embeds spatial and strategic affordances (pre‑training tacit behaviour) ^[4], then exposes them to an evolutionary generator of auxiliary adversarial attackers that iteratively hardens policy learning under diverse, adversarially‑perturbed environments ^[5]. Role specialization (Orchestrator, Executor, Ground, Critic, Memory) is instantiated per the debate‑based multi‑agent framework, ensuring that each agent’s output is subject to peer review and rebuttal, thereby reducing hallucination propagation ^[6].

Embodiment 2 – Hybrid Reputation Aggregation (HRA) for Federated Retraining
HRA integrates geometric anomaly detection with momentum‑based reputation scores, assigning trust weights to incoming model updates from distributed clients. Composable anomaly scores derived from SHAP‑weighted Byzantine detection are combined with a reputation vector that decays with sustained misbehavior, preventing poisoning of the shared model even when the adversary controls a minority of nodes ^[7]^[8].

Embodiment 3 – Trust‑Aware Sensor Fusion with Dynamic Field‑of‑View (TASF‑DFOV)
Sensor data from heterogeneous modalities (LiDAR, vision, radio) are mapped to trust pseudomeasurements, and a hidden‑Markov‑model‑based fusion engine updates trust PDFs conditioned on dynamic FOV estimates derived from ray‑tracing on point clouds. By weighting collaborative state estimation with per‑agent trust, a compromised node’s influence is attenuated while preserving high‑fidelity consensus among honest participants ^[9].

Embodiment 4 – Randomized Smoothing for LLM‑Based MAS (RS‑LLM‑MAS)
RS‑LLM‑MAS applies randomized smoothing to the output distribution of large language model agents, mitigating the propagation of adversarial hallucinations and ensuring that any injected malicious content is statistically bounded in its influence on subsequent coordination decisions. The technique is integrated into the MPAC multi‑principal coordination protocol, which governs inter‑principal message exchange, ensuring that no single principal can unilaterally dictate the joint policy ^[10]^[11].

Embodiment 5 – World‑Model Grounding Layer
The world‑model grounding layer enforces formal ontology constraints (RDF/OWL world models) to prevent hallucination‑induced operational failure ^[12], providing traceable decision justification and enabling runtime explainability.

Embodiment 6 – Resilient Agentic Coordination Engine (RACE)
RACE integrates the four innovations into a modular engine that operates in three layers: (i) world‑model grounding, (ii) trust‑aware communication (combining TASF‑DFOV and HRA), and (iii) dynamic adversarial learning (continuous refinement of DRAT policies and application of RS‑LLM‑MAS smoothing). The engine is scalable to thousands of agents, with sub‑linear communication and computation overhead as demonstrated by secure aggregation protocols such as RAIN ^[v5569] and GESAC ^[v10165].

CLAIMS

1. A method for resilient multi‑agent coordination comprising: (a) pre‑training each agent with a tacit mechanism that embeds spatial and strategic affordances; (b) exposing the agents to an evolutionary generator of auxiliary adversarial attackers that iteratively hardens policy learning; (c) assigning dynamic roles to agents (Orchestrator, Executor, Ground, Critic, Memory) and subjecting each agent’s output to peer review and rebuttal; (d) integrating geometric anomaly detection with momentum‑based reputation scores to assign trust weights to incoming model updates; (e) mapping heterogeneous sensor data to trust pseudomeasurements and updating trust PDFs conditioned on dynamic field‑of‑view estimates derived from ray‑tracing; (f) applying randomized smoothing to the output distribution of large language model agents; (g) enforcing formal ontology constraints on all agent decisions; and (h) continuously refining the agent policies and smoothing parameters based on runtime performance metrics, wherein the method guarantees Byzantine‑resilient convergence and sub‑linear scalability.

2. A resilient agentic coordination engine comprising: a world‑model grounding layer that enforces RDF/OWL ontology constraints; a trust‑aware communication layer that combines trust‑aware sensor fusion with dynamic field‑of‑view and hybrid reputation aggregation; and a dynamic adversarial learning layer that continuously refines dynamic role‑based adversarial training policies and applies randomized smoothing to large language model outputs, wherein the engine operates in a modular fashion across UAV swarms, cyber‑physical sensor networks, and decentralized finance ecosystems.

3. The method of claim 1, wherein the evolutionary generator of auxiliary adversarial attackers is implemented as a generative adversarial network that produces worst‑case perturbations for each agent.

4. The method of claim 1, wherein the role specialization is instantiated per a debate‑based multi‑agent framework that enables peer review and rebuttal of each agent’s output.

5. The method of claim 1, wherein the hybrid reputation aggregation assigns trust weights based on SHAP‑weighted Byzantine detection scores and a reputation vector that decays with sustained misbehavior.

6. The method of claim 1, wherein the trust‑aware sensor fusion employs a hidden‑Markov‑model‑based fusion engine that updates trust PDFs conditioned on dynamic field‑of‑view estimates derived from ray‑tracing on point clouds.

7. The method of claim 1, wherein the randomized smoothing is applied to the output distribution of large language model agents to statistically bound the influence of injected malicious content.

8. The method of claim 1, wherein the world‑model grounding layer enforces formal ontology constraints to prevent hallucination‑induced operational failure.

9. The method of claim 1, wherein the engine achieves sub‑linear communication overhead by employing secure aggregation protocols such as RAIN.

10. The system of claim 2, wherein the engine includes a runtime explainability module that logs each inference step and provides natural‑language explanations of agent decisions.

11. The system of claim 2, wherein the engine incorporates a continuous safety monitoring component that enforces safety constraints and triggers automated incident response upon detection of policy violations.

12. The system of claim 2, wherein the engine is deployable in a decentralized governance framework that issues cryptographically verifiable credentials to human sponsors and enforces least‑privilege access at runtime.

ABSTRACT

A resilient agentic coordination engine (RACE) for adaptive multi‑agent defense against adversarial coordination is disclosed. RACE fuses dynamic role‑based adversarial training, hybrid reputation aggregation, trust‑aware sensor fusion with dynamic field‑of‑view, and randomized smoothing for large language model agents within a three‑layer architecture comprising a world‑model grounding layer, a trust‑aware communication layer, and a dynamic adversarial learning layer. The engine guarantees Byzantine‑resilient convergence, provides runtime explainability through ontology‑based justification, and scales sub‑linearly to thousands of agents, enabling secure, interpretable coordination in UAV swarms, cyber‑physical sensor networks, and decentralized finance systems.

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Adaptive Multi‑Agent Defense Against Adversarial Coordination

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