The multiple-target self-organizing pursuit (SOP) problem has wide
applications and has been considered a challenging self-organization game for
distributed systems, in which intelligent agents cooperatively pursue multiple
dynamic targets with partial observations. This work proposes a framework for
decentralized multi-agent systems to improve intelligent agents' search and
pursuit capabilities. We model a self-organizing system as a partially
observable Markov game (POMG) with the features of decentralization, partial
observation, and noncommunication. The proposed distributed algorithm: fuzzy
self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the
three challenges in multi-target SOP: distributed self-organizing search (SOS),
distributed task allocation, and distributed single-target pursuit. FSC2
includes a coordinated multi-agent deep reinforcement learning method that
enables homogeneous agents to learn natural SOS patterns. Additionally, we
propose a fuzzy-based distributed task allocation method, which locally
decomposes multi-target SOP into several single-target pursuit problems. The
cooperative coevolution principle is employed to coordinate distributed
pursuers for each single-target pursuit problem. Therefore, the uncertainties
of inherent partial observation and distributed decision-making in the POMG can
be alleviated. The experimental results demonstrate that distributed
noncommunicating multi-agent coordination with partial observations in all
three subtasks are effective, and 2048 FSC2 agents can perform efficient
multi-target SOP with almost 100% capture rates