Cost Aware Asynchronous Multi-Agent Active Search
This work addresses the problem of efficient target detection with cost considerations for autonomous multi-agent systems, representing an incremental improvement by removing simplifications from prior methods.
The paper tackles the problem of multi-agent active search in unknown environments where agents must consider action costs, by introducing an online algorithm that combines Thompson Sampling, Monte Carlo Tree Search, and pareto-optimal confidence bounds to make adaptive, cost-aware decisions. The result is demonstrated through simulation analysis, showing efficacy in cost-aware active search, though no concrete numbers are provided.
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.