将符号自动机融入神经网络，提升时序任务的推理与泛化能力
NeSyA: Neurosymbolic Automata
- 用符号自动机建模时序逻辑，结合神经感知实现端到端可微
- 在合成数据上性能优于以往神经符号系统，真实任务中泛化更强
- 适合需要可解释性与时序推理的场景，如事件识别与轨迹分析
神经符号（NeSy）AI为融合神经与符号推理提供了新方向。然而，针对序列/时序问题的系统研究仍较少。本文认为符号自动机（结合有限状态机的时序推理与命题逻辑的静态推理能力）是表达时序领域知识的合适形式。聚焦序列分类与标注任务，我们展示了符号自动机可在概率语义下与基于神经的感知模块集成，构建端到端可微模型。提出的混合模型NeSyA（Neurosymbolic Automata）在合成基准上表现更优或更具扩展性，在真实世界事件识别任务中展现出优于纯神经系统的泛化能力。
原文摘要 · Abstract (English)
Neurosymbolic (NeSy) AI has emerged as a promising direction to integrate neural and symbolic reasoning. Unfortunately, little effort has been given to developing NeSy systems tailored to sequential/temporal problems. We identify symbolic automata (which combine the power of automata for temporal reasoning with that of propositional logic for static reasoning) as a suitable formalism for expressing knowledge in temporal domains. Focusing on the task of sequence classification and tagging we show that symbolic automata can be integrated with neural-based perception, under probabilistic semantics towards an end-to-end differentiable model. Our proposed hybrid model, termed NeSyA (Neuro Symbolic Automata) is shown to either scale or perform more accurately than previous NeSy systems in a synthetic benchmark and to provide benefits in terms of generalization compared to purely neural systems in a real-world event recognition task.
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