A Hybrid ANN–LSTM and Petri Net Framework for Proactive Deadlock Detection and Autonomous Resolution in Concurrent Systems

This paper introduces an integrated deep learning and formal verification framework for automated deadlock detection and resolution in intelligent concurrent systems. By leveraging Long Short-Term Memory (LSTM) networks trained via Backpropagation Through Time (BPTT), the proposed architecture predicts temporal task-resource conflicts with high accuracy. Petri nets serve as the foundational formalism for modeling task interactions and verifying system correctness. Upon deadlock detection, a novel rebuilding module dynamically adjusts execution paths to restore system operability without manual intervention. The framework is validated through a large-scale bridge control system simulation, demonstrating a significant reduction in both deadlock frequency and resolution latency. The proposed methodology offers an efficient and scalable alternative for ensuring correctness and adaptivity in real-time distributed environments.