# Cost Aware Asynchronous Multi-Agent Active Search

arXiv: https://arxiv.org/abs/2210.02259 · HTML: https://mlsanity.com/paper/2210.02259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Thompson Sampling for Complex Bandit Problems](https://mlsanity.com/paper/1311.0466/) (2013)
- [Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search](https://mlsanity.com/paper/1902.10182/) (2019)
- [Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2003.09746/) (2020)
- [Thompson Sampling is Asymptotically Optimal in General Environments](https://mlsanity.com/paper/1602.07905/) (2016)
- [Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoring](https://mlsanity.com/paper/1703.02854/) (2017)
- [Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning](https://mlsanity.com/paper/2111.01825/) (2021)
- [Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments](https://mlsanity.com/paper/1909.12216/) (2019)
- [Multi-agent active perception with prediction rewards](https://mlsanity.com/paper/2010.11835/) (2020)
- [Active Search for Sparse Signals with Region Sensing](https://mlsanity.com/paper/1612.00583/) (2016)
- [Multi-Agent Active Search using Realistic Depth-Aware Noise Model](https://mlsanity.com/paper/2011.04825/) (2020)
- [A Decision-theoretic Approach to Detection-based Target Search with a UAV](https://mlsanity.com/paper/1801.01228/) (2018)
- [Human-Centric Active Perception for Autonomous Observation](https://mlsanity.com/paper/2006.00037/) (2020)

## Abstract (arXiv)

Multi-agent active search requires autonomous agents to choose sensing actions that efficiently locate targets. In a realistic setting, agents also must consider the costs that their decisions incur. Previously proposed active search algorithms simplify the problem by ignoring uncertainty in the agent's environment, using myopic decision making, and/or overlooking costs. In this paper, we introduce an online active search algorithm to detect targets in an unknown environment by making adaptive cost-aware decisions regarding the agent's actions. Our algorithm combines principles from Thompson Sampling (for search space exploration and decentralized multi-agent decision making), Monte Carlo Tree Search (for long horizon planning) and pareto-optimal confidence bounds (for multi-objective optimization in an unknown environment) to propose an online lookahead planner that removes all the simplifications. We analyze the algorithm's performance in simulation to show its efficacy in cost aware active search.