  Decentralized planning is a key element of cooperative multi-agent systems
for information gathering tasks. However, despite the high frequency of agent
failures in realistic large deployment scenarios, current approaches perform
poorly in the presence of failures, by not converging at all, and/or by making
very inefficient use of resources (e.g. energy). In this work, we propose
Attritable MCTS (A-MCTS), a decentralized MCTS algorithm capable of timely and
efficient adaptation to changes in the set of active agents. It is based on the
use of a global reward function for the estimation of each agent's local
contribution, and regret matching for coordination. We evaluate its
effectiveness in realistic data-harvesting problems under different scenarios.
We show both theoretically and experimentally that A-MCTS enables efficient
adaptation even under high failure rates. Results suggest that, in the presence
of frequent failures, our solution improves substantially over the best
existing approaches in terms of global utility and scalability.
