The Legibility Methods in Agent Systems

To tackle problems in the domains of human-machine collaboration and multiagent cooperation (e.g., multiagent sequential decision-making, path planning, and navigation), “legibility” — the agent’s ability to convey its intentions through its behavior — can provides a reliable foundation for efficient and seamless collaboration. This paper systematically reviews the most recent research on legibility in agent-based scenarios, concentrating on its theoretical foundations, core methodologies, evaluation metrics, and future directions. The analysis demonstrates that existing methods (such as reward shaping, inverse reinforcement learning, and network flow optimization) have increased the efficiency of collaboration as well as the speed and success rate of intention inference. These methods hold both theoretical significance and practical value in real-world scenarios like warehouse management. But there are still issues with scalability in dynamic target scenarios and adaptability to partially observable environments. Future research could look at adaptive legibility techniques combined with inverse reinforcement learning as well as legibility coordination mechanisms in multiagent dynamic interactions.