Integrating Decision Sharing with Prediction in Decentralized Planning for
Multi-Agent Coordination under Uncertainty
Abstract
The performance of decentralized multi-agent systems tends to benefit from information sharing and
its effective utilization. However, too much or unnecessary sharing may hinder the performance due
to the delay, instability and additional overhead of
communications. Aiming to a satisfiable coordination performance, one would prefer the cost of
communications as less as possible. In this paper,
we propose an approach to improve the sharing utilization by integrating information sharing with
prediction in decentralized planning. We present
a novel planning algorithm by combining decision sharing and prediction based on decentralized
Monte Carlo Tree Search called Dec-MCTS-SP.
Each agent grows a search tree guided by the rewards calculated by the joint actions, which can not
only be sampled from the shared probability distributions over action sequences, but also be predicted by a sufficiently-accurate and computationallycheap heuristics-based method. Besides, several
policies including sparse and discounted UCT and
DIY-bonus are leveraged for performance improvement. We have implemented Dec-MCTS-SP in the
case study on multi-agent information gathering
under threat and uncertainty, which is formulated
as Decentralized Partially Observable Markov Decision Process (Dec-POMDP). The factored belief
vectors are integrated into Dec-MCTS-SP to handle the uncertainty. Comparing with the random,
auction-based algorithm and Dec-MCTS, the evaluation shows that Dec-MCTS-SP can reduce communication cost significantly while still achieving a
surprisingly higher coordination performance