Abstract
We present a scalable planning algorithm for multi-agent sequential decision problems that require dynamic collaboration.
Teams of agents need to coordinate decisions in many domains,
but naive approaches fail due to the exponential growth of the joint action space with the number of agents.
We circumvent this complexity through an anytime approach that allows us to trade computation for
approximation quality and also dynamically coordinate actions.
Our algorithm comprises three elements: online planning with Monte Carlo
Tree Search (MCTS), factorizing local agent interactions with coordination graphs, and
selecting optimal joint actions with the Max-Plus method.
On the benchmark SysAdmin domain with static coordination graphs, our approach achieves comparable performance with much lower computation cost than the MCTS baselines.
We also introduce a multi-drone delivery domain with dynamic, i.e., state-dependent coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods.
Keywords
- Artificial Intelligence: General
