Liu Ming, Xu Yang, Mohammed Abdul-Wahid
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China.
PLoS One. 2016 Jan 4;11(1):e0145526. doi: 10.1371/journal.pone.0145526. eCollection 2016.
Limited communication resources have gradually become a critical factor toward efficiency of decentralized large scale multi-agent coordination when both system scales up and tasks become more complex. In current researches, due to the agent's limited communication and observational capability, an agent in a decentralized setting can only choose a part of channels to access, but cannot perceive or share global information. Each agent's cooperative decision is based on the partial observation of the system state, and as such, uncertainty in the communication network is unavoidable. In this situation, it is a major challenge working out cooperative decision-making under uncertainty with only a partial observation of the environment. In this paper, we propose a decentralized approach that allows agents cooperatively search and independently choose channels. The key to our design is to build an up-to-date observation for each agent's view so that a local decision model is achievable in a large scale team coordination. We simplify the Dec-POMDP model problem, and each agent can jointly work out its communication policy in order to improve its local decision utilities for the choice of communication resources. Finally, we discuss an implicate resource competition game, and show that, there exists an approximate resources access tradeoff balance between agents. Based on this discovery, the tradeoff between real-time decision-making and the efficiency of cooperation using these channels can be well improved.
当系统规模扩大且任务变得更加复杂时，有限的通信资源已逐渐成为影响分散式大规模多智能体协调效率的关键因素。在当前的研究中，由于智能体的通信和观测能力有限，处于分散环境中的智能体只能选择部分信道进行访问，而无法感知或共享全局信息。每个智能体的协作决策都是基于对系统状态的部分观测，因此，通信网络中的不确定性是不可避免的。在这种情况下，仅通过对环境的部分观测来解决不确定性下的协作决策是一项重大挑战。在本文中，我们提出了一种分散式方法，允许智能体进行协作搜索并独立选择信道。我们设计的关键在于为每个智能体的视角构建最新的观测，以便在大规模团队协调中实现局部决策模型。我们简化了分散式部分可观测马尔可夫决策过程（Dec-POMDP）模型问题，每个智能体可以共同制定其通信策略，以提高其在选择通信资源时的局部决策效用。最后，我们讨论了一个隐含的资源竞争博弈，并表明，智能体之间存在近似的资源访问权衡平衡。基于这一发现，可以很好地改善实时决策与使用这些信道的合作效率之间的权衡。
