Multi-Agent Decision-Support in Adversarial Environments

With my team at Multi AI, I have developed a Multi-Agent Coordination Platform for large-scale transportation and inventory problems in the military. This high-fidelity platform assists wargamers and logisticians in testing and optimizing their transportation and inventory plans against real-world scenarios.

Why This Platform?

Traditional military logistics tools fail to account for disturbances and adversarial activities in their logistics planning. They ignore the bi-directional dependencies between forward operations and logistics, often leading to severe supply bottlenecks. Our platform allows logisticians and wargamers to overcome these challenges by providing an extendable, customizable, and AI-integrated simulation framework that can be used to analyze and optimize theater-wide transportation and inventory plans.

Key Features:

• Agent-Based Digital Twin Simulation: Models complex logistics and forward operations and their sensitivity to disturbances and adversarial activities.
• Customizable Scenarios: Users can input their specific logistics scenarios and rapidly test various real-world conditions.
• AI Decision Support: Interfaces with advanced AI to analyze and optimize logistics and forward operations plans to mitigate risk and vulnerabilities.

Breakthrough in Decentralized Decision-Making

The Multi-Agent Coordination Platform uses cutting-edge AI to assist with the user’s decision-making in optimizing complex transportation and inventory plans for adversarial environments. The platform uses novel Artificial Intelligence algorithms, including Decentralized Monte Carlo Tree Search (MCTS) and Generative AI, to find near-optimal solutions within minutes.

Highlights of Our Algorithms:

• Decentralized Stochastic MCTS: Each agent maintains a separate search tree, enabling scalable and distributed decision-making. Here, agents represent the individual cargo and forward-operating vehicles in the environment.
• Stochastic Optimization: The MCTS uses chance nodes to incorporate uncertain logistics conditions in adversarial environments.
• Hierarchical Planning: Multi-level decision-making to optimize global objectives while allowing individual agents to react to local disruptions.
• Policy Learning and Adaptation: Uses reinforcement learning to refine strategies dynamically based on real-time feedback.

Real-World Impact: Military Ground Tests

We rigorously tested our Multi-Agent Coordination Platform in real-world logistics scenarios with our AFWERX Autonomy Prime partners. By using the Multi-Agent Coordination Platform to coordinate vehicles in a user-specified scenario, we demonstrated 38% more resources delivered in scenarios where vehicles would randomly break down or encounter operational difficulties and 36% more resources delivered in scenarios where vehicles would significantly deviate from their nominal speeds due to disturbances. These tests showcase the platform’s many benefits:

• Optimized Resource Allocation: The platform dynamically re-routes resources to where they are most needed.
• Adaptive Strategies: AI-generated transportation and inventory plans adjust in real time to disturbances and adversarial activities to minimize loss of vehicles and resources.
• Computational Efficiency: The platform computes solutions within minutes that would otherwise be impossible for a logistician or wargamer to plan by hand or with conventional tools.