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Cost Aware Asynchronous Multi-Agent Active Search

arXiv 2210.02259 ↗First posted 5 Oct 2022cs.LG cs.MA cs.ROcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 51 references that are in the map. First posted on arXiv 5 Oct 2022.

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References · 14

  1. 2017A Tutorial on Thompson Samplingcited by 420
  2. 2013The Complexity of Decentralized Control of Markov Decision Processescited by 225
  3. 2013Thompson Sampling for Complex Bandit Problemscited by 43
  4. 2019Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Searchcited by 17
  5. 2016Thompson Sampling is Asymptotically Optimal in General Environmentscited by 8
  6. 2017Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoringcited by 8
  7. 2020Adaptive Informative Path Planning with Multimodal Sensingcited by 7
  8. 2021Pareto Monte Carlo Tree Search for Multi-Objective Informative Planningcited by 7
  9. 2019Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environmentscited by 6
  10. 2016Active Search for Sparse Signals with Region Sensingcited by 4
  11. 2020Multi-agent active perception with prediction rewardscited by 4
  12. 2020Multi-Agent Active Search using Realistic Depth-Aware Noise Modelcited by 4
2 more
  1. 2018A Decision-theoretic Approach to Detection-based Target Search with a UAVcited by 3
  2. 2020Human-Centric Active Perception for Autonomous Observationcited by 2

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