# Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents

arXiv: https://arxiv.org/abs/2401.03154 · HTML: https://mlsanity.com/paper/2401.03154/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [An informative path planning framework for UAV-based terrain monitoring](https://mlsanity.com/paper/1809.03870/) (2018)
- [Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection](https://mlsanity.com/paper/2105.08601/) (2021)
- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [A Cooperative Multi-Agent Probabilistic Framework for Search and Track Missions](https://mlsanity.com/paper/2302.10723/) (2023)
- [Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects](https://mlsanity.com/paper/2203.04551/) (2022)
- [Regenerative Particle Thompson Sampling](https://mlsanity.com/paper/2203.08082/) (2022)
- [GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search](https://mlsanity.com/paper/2304.02075/) (2023)

## Built on by (most cited first, 2 of 2)

- [Challenges Faced by Large Language Models in Solving Multi-Agent Flocking](https://mlsanity.com/paper/2404.04752/) (2024)
- [Active Scout: Multi-Target Tracking Using Neural Radiance Fields in Dense Urban Environments](https://mlsanity.com/paper/2406.07431/) (2024)

## Abstract (arXiv)

Multi-agent multi-target tracking has a wide range of applications, including wildlife patrolling, security surveillance or environment monitoring. Such algorithms often make restrictive assumptions: the number of targets and/or their initial locations may be assumed known, or agents may be pre-assigned to monitor disjoint partitions of the environment, reducing the burden of exploration. This also limits applicability when there are fewer agents than targets, since agents are unable to continuously follow the targets in their fields of view. Multi-agent tracking algorithms additionally assume inter-agent synchronization of observations, or the presence of a central controller to coordinate joint actions. Instead, we focus on the setting of decentralized multi-agent, multi-target, simultaneous active search-and-tracking with asynchronous inter-agent communication. Our proposed algorithm DecSTER uses a sequential monte carlo implementation of the probability hypothesis density filter for posterior inference combined with Thompson sampling for decentralized multi-agent decision making. We compare different action selection policies, focusing on scenarios where targets outnumber agents. In simulation, we demonstrate that DecSTER is robust to unreliable inter-agent communication and outperforms information-greedy baselines in terms of the Optimal Sub-Pattern Assignment (OSPA) metric for different numbers of targets and varying teamsizes.