Cost Aware Asynchronous Multi-Agent Active Search

TL;DR

This paper presents a novel online active search algorithm for multi-agent systems that effectively balances target detection with cost considerations in unknown environments, using advanced decision-making techniques.

Contribution

It introduces a comprehensive cost-aware active search algorithm combining Thompson Sampling, Monte Carlo Tree Search, and Pareto-optimal bounds, removing previous simplifications.

Findings

Algorithm demonstrates effective target detection in simulations.

Balances exploration and exploitation with cost considerations.

Outperforms simpler methods in cost-aware scenarios.

Abstract

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.