Interactive Adjustment of Agent-Based Cooperative Tasks Driven by Multi-View Collaboration

Interactive adjustment of agent-based cooperative tasks is essential for supporting flexible and complex task execution. However, existing approaches generally lack intuitive visual and effective interaction, which limits real-time task adjustment and human intervention in complex scenarios. To address these challenges, we propose an interactive adjustment framework for agent-based cooperative tasks driven by multi-view collaboration. First, we propose a task representation visualization method based on a Dual-Structured Graph to provide a unified representation of task structures and agent execution processes. Second, we propose a task interaction enhancement method driven by a large language model to accurately interpret and execute diverse user interaction commands. Building on these methods, we implement an integrated platform with three views to support intuitive and flexible task adjustment through multi-view collaboration. The effectiveness of the proposed framework and platform is validated through three usage scenarios and a user study.