Cellular inference network (CIN) for learning and control of multi-agent cooperation

The coordination and learning process in a multiagent system (MAS) can be modeled as a control and adaptation process of a hybrid dynamic system. We study an inference network organized in cellular structure for supporting multiple levels of abstraction and reasoning in learning and control of MAS. Specifically, we study how the cellular inference structure can serve as a representation of hybrid dynamic systems model and be applied to the knowledge gathering and refinement processes of the MAS. The study shows that it is a promising model for distributed reasoning in the presence of uncertainties in the domains of knowledge management, deconfliction, and integration.