Multi-agent systems manage task dependencies by using structured communication protocols, coordination mechanisms, and shared knowledge. These systems consist of multiple autonomous agents that can perform tasks independently but often need to collaborate to achieve complex objectives. By recognizing dependencies, agents can decide which tasks need to be completed before others can start, ensuring a smooth workflow.
One common approach to managing task dependencies is through the use of a centralized coordinator or a shared task schedule. For example, agents can send messages to a central hub to report their status on specific tasks and to inquire about the readiness of dependent tasks. If Agent A needs to finish its part before Agent B can begin, Agent A will communicate its completion status to the central hub, which will then notify Agent B to proceed. This coordination minimizes delays and keeps the system organized.
Another method is to implement a dependency graph, where tasks are represented as nodes and dependencies are the edges connecting them. Each agent can access this graph to identify which tasks are waiting on others. For instance, in a manufacturing scenario, if Task 1 is to assemble a part but Task 2 (painting) must be completed first, agents can visually or programmatically trace the dependencies to prioritize their work accordingly. By using such structures, multi-agent systems efficiently manage dependencies, ensuring that tasks are executed in the correct order and resources are utilized effectively.