Background
In multi-intelligence systems, inefficient collaboration usually stems from problems such as communication delays, uneven task distribution, or conflicting decision-making, etc. Quantum Swarm, a specially designed multi-intelligence framework, has built-in multiple optimization mechanisms to improve collaboration efficiency.
Core Solutions
The following are three possible optimization methods:
- Using the built-in scheduling algorithm: The framework provides the following algorithms to be called directly:
- Priority use
env.set_scheduler('dynamic')Enable dynamic tasking - or through
env.set_communication('pubsub')Setting up publish-subscribe communication mode
- Priority use
- Customizing Collaboration Strategies::
- Overriding in the Agent class
collaborate()methodologies - Implementing a utility-based decision matrix
- pass (a bill or inspection etc)
env.share_data()Realization of information sharing
- Overriding in the Agent class
- Debugging with visualization tools::
- (of a computer) run
env.enable_visualizer()Activate real-time monitoring - Focus on idle metrics in the Agent state matrix
- Adjusting Decision Threshold Parameters to Optimize Collaboration Frequency
- (of a computer) run
Summary points
It is recommended to try the framework's preset collaboration patterns first, and then consider customizing the strategy when encountering specific scenarios. Regular use of data analysis tools (env.get_stats()) Evaluate the optimization effect and form a closed loop of iterative improvement.
This answer comes from the articleQuantum Swarm: a framework for multi-intelligence cluster collaborationThe
