Nature of the problem
Resource deadlocks typically occur when multiple intelligences loop waiting for resources occupied by each other. The framework provides three levels of prevention.
prescription
Select the appropriate scenario based on the complexity of the scenario:
- prophylaxis(Good for simple scenes):
- Enable resource sorting:
env.enable_resource_ordering() - Sets the request timeout:
agent.set_timeout(5.0) - Use atomic operations:
env.lock_resource()/unlock()
- Enable resource sorting:
- Intermediate testing(Recommended Programs):
- Configure the deadlock detector:
deadlock_checker = DeadlockDetector(env) - Set the inspection interval:
deadlock_checker.set_interval(2.0) - Registers the processing callbacks:
deadlock_checker.on_deadlock(callback)
- Configure the deadlock detector:
- Advanced circumvention(complex systems):
- Implementing the banker's algorithm: inheritance
ResourceManagerresemble - Use predictive allocation:
agent.predictive_acquire() - Introduction of an auction mechanism:
env.enable_bidding_system()
- Implementing the banker's algorithm: inheritance
Debugging Tips
Can be used when there is a deadlock:env.diagnose_deadlock()generating a dependency graph.env.break_deadlock()Forced release. Recommended for test environmentsenv.inject_faults()Proactive exception triggering to validate the protection mechanism.
This answer comes from the articleQuantum Swarm: a framework for multi-intelligence cluster collaborationThe
May not be reproduced without permission:AI productivity tools " How to avoid resource deadlocks for Quantum Swarm intelligences in competitive scenarios?
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