  Multi-agent multi-target tracking has a wide range of applications, including
wildlife patrolling, security surveillance or environment monitoring. Such
algorithms often make restrictive assumptions: the number of targets and/or
their initial locations may be assumed known, or agents may be pre-assigned to
monitor disjoint partitions of the environment, reducing the burden of
exploration. This also limits applicability when there are fewer agents than
targets, since agents are unable to continuously follow the targets in their
fields of view. Multi-agent tracking algorithms additionally assume inter-agent
synchronization of observations, or the presence of a central controller to
coordinate joint actions. Instead, we focus on the setting of decentralized
multi-agent, multi-target, simultaneous active search-and-tracking with
asynchronous inter-agent communication. Our proposed algorithm DecSTER uses a
sequential monte carlo implementation of the probability hypothesis density
filter for posterior inference combined with Thompson sampling for
decentralized multi-agent decision making. We compare different action
selection policies, focusing on scenarios where targets outnumber agents. In
simulation, we demonstrate that DecSTER is robust to unreliable inter-agent
communication and outperforms information-greedy baselines in terms of the
Optimal Sub-Pattern Assignment (OSPA) metric for different numbers of targets
and varying teamsizes.

Robotics;Artificial Intelligence;Machine Learning;Multiagent Systems