Liu Wei, Ran Weizhi, Nantogma Sulemana, Xu Yang
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Entropy (Basel). 2021 Mar 14;23(3):342. doi: 10.3390/e23030342.
Decentralization is a peculiar characteristic of self-organizing systems such as swarm intelligence systems, which function as complex collective responsive systems without central control and operates based on contextual local coordination among relatively simple individual systems. The decentralized particularity of self-organizing systems lies in their capacity to spontaneously respond to accommodate environmental changes in a cooperative manner without external control. However, if members cannot obtain observations of the state of the whole team and environment, they have to share their knowledge and policies with each other through communication in order to adapt to the environment appropriately. In this paper, we propose an information sharing mechanism as an independent decision phase to improve individual members' joint adaption to the world to fulfill an optimal self-organization in general. We design the information sharing decision analogous to human information sharing mechanisms. In this case, information can be shared among individual members by evaluating the semantic relationship of information based on ontology graph and their local knowledge. That is, if individual member collects more relevant information, the information will be used to update its local knowledge and improve sharing relevant information by measuring the ontological relevance. This will enable more related information to be acquired so that their models will be reinforced for more precise information sharing. Our simulations and experimental results show that this design can share information efficiently to achieve optimal adaptive self-organizing systems.
去中心化是诸如群体智能系统等自组织系统的一个独特特征，这些系统作为复杂的集体响应系统运行，无需中央控制，并基于相对简单的个体系统之间的上下文局部协调进行操作。自组织系统的去中心化特性在于它们能够在没有外部控制的情况下，以合作的方式自发响应以适应环境变化。然而，如果成员无法获取整个团队和环境状态的观测信息，他们就必须通过通信相互分享知识和策略，以便适当地适应环境。在本文中，我们提出一种信息共享机制作为一个独立的决策阶段，以总体上提高个体成员对世界的联合适应能力，从而实现最优的自组织。我们设计的信息共享决策类似于人类信息共享机制。在这种情况下，可以通过基于本体图评估信息的语义关系及其局部知识，在个体成员之间共享信息。也就是说，如果个体成员收集到更多相关信息，该信息将用于更新其局部知识，并通过测量本体相关性来改进相关信息的共享。这将使得能够获取更多相关信息，从而强化其模型以进行更精确的信息共享。我们的模拟和实验结果表明，这种设计能够高效地共享信息，以实现最优的自适应自组织系统。
