# United We Stand: Decentralized Multi-Agent Planning With Attrition

arXiv: https://arxiv.org/abs/2407.08254 · HTML: https://mlsanity.com/paper/2407.08254/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Concurrent bandits and cognitive radio networks](https://mlsanity.com/paper/1404.5421/) (2014)
- [Multi-Player Bandits: A Trekking Approach](https://mlsanity.com/paper/1809.06040/) (2018)
- [Distributed Strategy Selection: A Submodular Set Function Maximization Approach](https://mlsanity.com/paper/2107.14371/) (2021)
- [Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems](https://mlsanity.com/paper/1911.08642/) (2019)
- [Decentralized MCTS via Learned Teammate Models](https://mlsanity.com/paper/2003.08727/) (2020)
- [Exploiting Submodular Value Functions For Scaling Up Active Perception](https://mlsanity.com/paper/2009.09696/) (2020)
- [Multi-agent active perception with prediction rewards](https://mlsanity.com/paper/2010.11835/) (2020)
- [Scalable Anytime Planning for Multi-Agent MDPs](https://mlsanity.com/paper/2101.04788/) (2021)
- [Attrition-Aware Adaptation for Multi-Agent Patrolling](https://mlsanity.com/paper/2304.01386/) (2023)
- [Social Optimum Equilibrium Selection for Distributed Multi-Agent Optimization](https://mlsanity.com/paper/2307.13242/) (2023)

## Built on by (most cited first, 1 of 1)

- [Boltzmann-based Exploration for Robust Decentralized Multi-Agent Planning (Extended Version)](https://mlsanity.com/paper/2603.02154/) (2026)

## Abstract (arXiv)

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.