Intelligent Knowledge Distribution: Constrained-Action POMDPs for Resource-Aware Multiagent Communication.

Area of Science:

• Robotics and Artificial Intelligence
• Distributed Systems
• Optimization Theory

Background:

• Multiagent systems require efficient knowledge distribution under resource constraints.
• Maintaining environmental awareness and agent states is crucial but communication-intensive.
• Partially Observable Markov Decision Processes (POMDPs) are a common framework for such problems.

Purpose of the Study:

• To develop a novel framework for intelligent knowledge distribution in multiagent systems.
• To address communication constraints in terms of power and bandwidth.
• To improve coordination efficiency and reduce resource wastage.

Main Methods:

• Introduced constrained-action POMDPs (CA-POMDPs) with action-based constraints.
• Utilized finite-state controllers (FSCs) for infinite-horizon constraint analysis.
• Combined Markov chain Monte Carlo and discrete optimization for probabilistic constraint satisfaction.
• Proposed Intelligent Knowledge Distribution (IKD) for per-agent policies.

Main Results:

• The IKD model successfully maintained asset tracking in a UAV collaboration scenario.
• IKD violated soft power and bandwidth constraints only 3% of the time.
• Compared to greedy and naive methods, IKD showed a significant improvement, reducing constraint violations by over 57%.

Conclusions:

• CA-POMDPs and IKD provide an effective solution for resource-constrained multiagent communication.
• The proposed methods enable efficient knowledge distribution while satisfying operational constraints.
• IKD offers a substantial advantage over traditional approaches in complex multiagent coordination tasks.