融合神经网络与符号系统，提升AI可解释性与推理能力
Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era
- 将神经网络与符号计算结合，增强模型行为理解
- 在自然语言与视觉任务中提升推理能力与可解释性
- 适合关注AI可解释性与可信推理的研究者
自人工智能理论初现以来，神经符号（NeSy）方法始终吸引着研究者关注。其通过推断或利用行为模式，被视为实现人类级智能的可能途径。然而，语义泛化能力有限，以及在预设规则复杂场景中的应用挑战，限制了其在现实世界中的落地。自2017年连接主义系统取得突破性进展以来，学界开始质疑NeSy方案在自然语言处理与计算机视觉等领域的竞争力。本综述聚焦任务导向的NeSy进展，探讨符号系统如何增强可解释性与推理能力。研究结果旨在为探索真实任务中可解释性NeSy方法的研究者提供参考。所有可复现细节及对每项研究的深入评述均可在 https://github.com/disi-unibo-nlp/task-oriented-neuro-symbolic.git 获取。
原文摘要 · Abstract (English)
The integration of symbolic computing with neural networks has intrigued researchers since the first theorizations of Artificial intelligence (AI). The ability of Neuro-Symbolic (NeSy) methods to infer or exploit behavioral schema has been widely considered as one of the possible proxies for human-level intelligence. However, the limited semantic generalizability and the challenges in declining complex domains with pre-defined patterns and rules hinder their practical implementation in real-world scenarios. The unprecedented results achieved by connectionist systems since the last AI breakthrough in 2017 have raised questions about the competitiveness of NeSy solutions, with particular emphasis on the Natural Language Processing and Computer Vision fields. This survey examines task-specific advancements in the NeSy domain to explore how incorporating symbolic systems can enhance explainability and reasoning capabilities. Our findings are meant to serve as a resource for researchers exploring explainable NeSy methodologies for real-life tasks and applications. Reproducibility details and in-depth comments on each surveyed research work are made available at https://github.com/disi-unibo-nlp/task-oriented-neuro-symbolic.git.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。