The Collective Intelligence for Advancing 6G Communications

The fifth-generation cellular networks (5G) has boosted the unprecedented convergence between the information world and physical world. On the other hand, empowered with the enormous amount of data and information, artificial intelligence (AI) has been universally applied and pervasive AI is believed to be an integral part of the future cellular networks (e.g., beyond 5G, B5G or the possible sixth-generation cellular networks, 6G). Consequently, benefiting from the advancement in communication technology and AI, we boldly argue that the conditions for collective intelligence (CI) will be mature in the B5G/6G era and CI will emerge among the widely connected beings and things. Afterwards, we introduce a regular language (i.e., the information economy metalanguage) supporting the future communications among agents and augment human intelligence. Meanwhile, we demonstrate the achievement of agents in a simulated scenario where the agents collectively work together to form a pattern through simple indirect communications. Finally, we discuss an anytime universal intelligence test model to evaluate the intelligence level of collective agents.

Brain-Inspired Stigmergy Learning

Stigmergy has proved its great superiority in terms of distributed control, robustness and adaptability, thus being regarded as an ideal solution for large-scale swarm control problems. Based on new discoveries on astrocytes in regulating synaptic transmission in the brain, this paper has mapped stigmergy mechanism into the interaction between synapses and investigated its characteristics and advantages. Particularly, we have divided the interaction between synapses which are not directly connected into three phases and proposed a stigmergic learning model. In this model, the state change of a stigmergy agent will expand its influence to affect the states of others. The strength of the interaction is determined by the level of neural activity as well as the distance between stigmergy agents. Inspired by the morphological and functional changes in astrocytes during environmental enrichment, it is likely that the regulation of distance between stigmergy agents plays a critical role in the stigmergy learning process. Simulation results have verified its importance and indicated that the well-regulated distance between stigmergy agents can help to obtain stigmergy learning gain.