用逻辑规则复用实现神经符号学习的持续进化
Neuro-Logic Lifelong Learning
- 基于逻辑规则的可组合性设计持续学习框架
- 新任务中复用旧任务规则，提升学习效率与性能
- 适合研究神经符号系统持续学习的学者参考
在神经符号人工智能中，用神经网络解决归纳逻辑编程（ILP）问题是关键挑战。尽管多数研究聚焦于为单一问题设计新网络架构，较少关注一系列问题上的新型学习范式。本文探索了终身学习型ILP，利用逻辑规则的组合性与可迁移性，实现对新问题的高效学习。我们提出一种组合式框架，证明早期任务中学到的逻辑规则可在后续任务中高效复用，从而提升可扩展性与性能。通过形式化方法并在任务序列上进行实证评估，结果验证了该范式的可行性与优势，为神经符号AI中的持续学习开辟了新方向。
原文摘要 · Abstract (English)
Solving Inductive Logic Programming (ILP) problems with neural networks is a key challenge in Neural-Symbolic Ar- tificial Intelligence (AI). While most research has focused on designing novel network architectures for individual prob- lems, less effort has been devoted to exploring new learning paradigms involving a sequence of problems. In this work, we investigate lifelong learning ILP, which leverages the com- positional and transferable nature of logic rules for efficient learning of new problems. We introduce a compositional framework, demonstrating how logic rules acquired from ear- lier tasks can be efficiently reused in subsequent ones, leading to improved scalability and performance. We formalize our approach and empirically evaluate it on sequences of tasks. Experimental results validate the feasibility and advantages of this paradigm, opening new directions for continual learn- ing in Neural-Symbolic AI.
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