FOT-LTN extends Logic Tensor Networks by integrating first-order linear temporal logic syntax with fuzzy differentiable semantics, supporting temporal operators and quantifiers, and shows improved performance over neural baselines on synthetic temporal knowledge graph completion tasks.
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A neuro-symbolic framework compiles LTLf formulas to DFAs, derives differentiable satisfaction signals from DFA progression, and uses them as a logic-based regularization loss to enforce temporal constraints in autoregressive transformer RL policies while preserving competitive returns.
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First-Order Temporal Logic Tensor Networks
FOT-LTN extends Logic Tensor Networks by integrating first-order linear temporal logic syntax with fuzzy differentiable semantics, supporting temporal operators and quantifiers, and shows improved performance over neural baselines on synthetic temporal knowledge graph completion tasks.
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Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies
A neuro-symbolic framework compiles LTLf formulas to DFAs, derives differentiable satisfaction signals from DFA progression, and uses them as a logic-based regularization loss to enforce temporal constraints in autoregressive transformer RL policies while preserving competitive returns.