Optimizing Agent Collaboration through Heuristic Multi-Agent Planning
This addresses a specific bottleneck in multi-agent planning for scenarios with varied sensing agents, representing an incremental improvement.
The paper tackles the problem of QDec-POMDP algorithms failing with heterogeneous sensing agents by introducing a new algorithm that enforces plan alignment when sensing capabilities differ, achieving significantly better performance than existing methods.
The SOTA algorithms for addressing QDec-POMDP issues, QDec-FP and QDec-FPS, are unable to effectively tackle problems that involve different types of sensing agents. We propose a new algorithm that addresses this issue by requiring agents to adopt the same plan if one agent is unable to take a sensing action but the other can. Our algorithm performs significantly better than both QDec-FP and QDec-FPS in these types of situations.