MULTI-AGENT SYSTEM BASED ON BI-LEVEL FBS SELF-CLOSED-LOOP CO-EVOLUTION FRAMEWORK

Multi-agent systems must operate in increasingly dynamic environments, where sudden changes such as the appearance of moving or unforeseen obstacles pose major challenges to the system’s adaptability. In this paper, we propose a bi-level Function-Behavior-Structure (FBS) self-closed-loop framework to enable both individual agents and clusters of agents to reason adaptively from task requirements and to change their behavior in response to environmental changes. Inspired by the FBS model in product design, this framework introduces self-reasoning at two hierarchical levels: the cluster-level and the individual agent-level. To enhance real-time adaptability, we develop an environment prediction method based on incremental changes in the location of obstacle pixels observed across time steps. This allows for the construction of dynamic prediction maps. Key to this is the introduction of a system event influence value, to quantify environmental disruptions and determine whether the system should perform global re-planning, local re-planning, or continue with the current plan, This helps the researcher establish a balance between responsiveness and computational efficiency. The framework is demonstrated with a collaborative box-pushing scenario involving multiple agents navigating evolving environments with successive levels of complexity. Comparative experiments show that the proposed method outperforms a traditional bi-level planning approach in both success rate and computational resource usage, particularly in high-complexity scenarios. The framework offers a generalizable approach to adaptive design for multi-agent systems and can be applied to domains such as smart manufacturing, logistics, and autonomous operations.