Abstract
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 information 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.
Highlights
Unmanned Aerial Vehicles (UAVs) are increasingly becoming essential tools to carry out situational awareness tasks in a number of real-world applications ranging from disaster response [1,2,3] and security surveillance [3,4,5]
We evaluate our algorithms in simulations and show that our algorithm outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents
For the multi-agent algorithm, we extended the sequential policy computation method for individual agents to deal with partially observable problems
Summary
Unmanned Aerial Vehicles (UAVs) are increasingly becoming essential tools to carry out situational awareness tasks in a number of real-world applications ranging from disaster response [1,2,3] and security surveillance [3,4,5]. Most of the work [3, 7, 8] focus on developing algorithms for UAVs gathering information in dynamic environments where the model of the features of the environment is fully observable and stationary (see Related Work section for more details) None of these approaches have considered how threats may affect the information gathering process while the environment is partially observable and non-stationary. Operating in uncertain environments, autonomous agents have to deal with executing actions that may not have the intended results, with environments that change while the agent is operating, and with making observations that might not be completely accurate Against this background, we propose a agent-based model for patrolling under uncertainty and threats and go on to develop a novel algorithm to solve the planning problem that it poses. We propose our multi-agent algorithm and evaluate it in the simulations of multi-agent patrolling in a large environment
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