Multi-Agent Patrolling under Uncertainty and Threats

Shaofei Chen, Feng Wu, Lincheng Shen, Jing Chen, Sarvapali D. Ramchurn, Yong Deng
2015 PLoS ONE  
We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much
more » ... ion as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.
doi:10.1371/journal.pone.0130154 pmid:26086946 pmcid:PMC4472811 fatcat:qbjpza6ctvcvnmg7b645qjklrm