This repository implements a Human-Model-Free adaptive agent architecture. Instead of relying on rigid, computationally expensive human behavioral models, the system leverages real-time trajectory analysis and the Cross-Entropy Method (CEM) to dynamically adapt agent policies. This guarantees resilient Human-Agent Teaming (HAT) even under noisy, volatile, or sub-optimal human behavior.
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Human-Model-Free Inference: Uses a sliding window over human actions to infer intent and select the optimal complementary agent policy in real time (
$< 50\text{ ms}$ ). -
Cross-Entropy Metric (CEM): Computes log-likelihood over human trajectories against a pre-trained policy library for implicit intent recognition without communication overhead.
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Smart Company (SmC) AOEA: Implements Agent-Oriented Enterprise Architecture using specialized FIPA-compliant nodes:
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MAA (Member Advisor Agent): Manages local unit resources and autonomous contract negotiations.
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GAA (Group Advisor Agent): Coordinates global supply chains and cross-agent ontology mapping.
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Zero-Friction Adaptation: Mitigates reward hacking and gracefully accommodates human cognitive shifts during complex collaborative tasks (e.g., Team Space Fortress benchmarks).
The inference engine evaluates the Cross-Entropy similarity for a human trajectory
Once the implicit human policy
# Clone the repository
git clone https://github.com/Sega757/Sovereign-Agent-Swarm.git
cd Sovereign-Agent-Swarm
# Install dependencies
pip install -r requirements.txt
# Run the adaptive inference simulation
python -m core.inference_engine
Sovereign-Agent-Swarm/
├── README.md # Project overview and documentation
├── requirements.txt # Project dependencies
├── docs/
│ └── whitepaper.md # Technical whitepaper on HAT & Smart Company
└── core/
├── __init__.py
├── inference_engine.py # CEM-based dynamic adaptation engine
└── smart_company.py # MAA & GAA multi-agent coordination logic