  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.
