linear-temporal-logic

A method for contract-based compositional shielding that ensures global safety in multi-agent reinforcement learning without centralized runtime control, using local LTL obligations and a multi-armed bandit to optimize team reward.

Introduces a neurosymbolic framework that injects LTLf constraints into transformer-based reinforcement learning policies via differentiable automaton representations and a logic-based loss, improving constraint satisfaction while maintaining competitive returns.

NeuroNL2LTL is a neurosymbolic framework that translates natural language to Linear Temporal Logic (LTL) using a two-stage architecture with verifier-in-the-loop training, achieving improved correctness guarantees for safety-critical specifications.