Implicit Cooperative Decision-Making for Unknown Area Exploration of Multi-agent Systems

Multi-agent systems have gained widespread applications across various fields, with collaborative decision-making standing as a key research focus. Mainstream algorithms primarily employ explicit cooperation, relying on continuous communication and information sharing among agents. However, in real-world environments, communication is often constrained, making implicit cooperation more practical. Implicit cooperation emphasizes collaboration through observation. This paper proposes a role-based algorithm for implicit collaborative decision-making of multi-agent systems. Initially, multiple agents are assigned as roles based on the overall task. Moreover, considering temporal logic characteristics of tasks, we utilize linear temporal logic language to express tasks and employ corresponding finite automaton for state transition. Then we design a role-based state estimation algorithm that estimates the environment’s state under limited communication conditions. Above all, we get an implicit cooperative decision-making framework to solve unknown area exploration task. Finally, simulation verifies the algorithm’s effectiveness.