[1]李昌原,程恺,郝文宁,等.面向人工智能领域的组合泛化方法研究综述[J].智能系统学报,2026,21(4):815-833.[doi:10.11992/tis.202507001]
LI Changyuan,CHENG Kai,HAO Wenning,et al.Comprehensive survey on compositional generalization methodologies in artificial intelligence[J].CAAI Transactions on Intelligent Systems,2026,21(4):815-833.[doi:10.11992/tis.202507001]
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LI Changyuan,CHENG Kai,HAO Wenning,et al.Comprehensive survey on compositional generalization methodologies in artificial intelligence[J].CAAI Transactions on Intelligent Systems,2026,21(4):815-833.[doi:10.11992/tis.202507001]
面向人工智能领域的组合泛化方法研究综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第4期
页码:
815-833
栏目:
综述
出版日期:
2026-07-05
- Title:
- Comprehensive survey on compositional generalization methodologies in artificial intelligence
- Keywords:
- compositionality; compositional generalization; artificial intelligence; foundational representation architecture; relational modeling architecture; learning mechanism architecture; machine learning; meta-learning
- 分类号:
- TP183; TP181; TP391.1
- 摘要:
- 组合性是现代人工智能系统所缺失的、人类智能的一个重要方面，能够增强系统泛化和解决新颖且复杂任务的能力。针对组合泛化概念内涵模糊化、方法分类碎片化、机理阐释表层化等问题，明确了组合泛化的基本概念，从系统性、可组合性、可解释性和动态适应性等4个方面分析了其特征，提出了基于架构演进的组合泛化方法分类体系，按照基础表征架构、关系建模架构与学习机制架构对现有组合方法进行了分类，详细阐述了每种方法的模型结构与组合泛化之间的机理，进一步比较了不同方法之间的优缺点及场景适用性。针对组合泛化面临的动态性、知识依赖、计算复杂、异质关系等问题，提出了可行的解决思路，为该领域的深入研究提供了指导。
- Abstract:
- Compositionality, a critical aspect of human intelligence, is often lacking in modern artificial intelligence systems. Its integration can considerably enhance a system’s generalization capabilities and its ability to solve novel and complex tasks. This study addresses the current challenges of a vague understanding of compositional generalization, fragmented method classification, and superficial explanations of underlying mechanisms. The study begins by clarifying the fundamental concept of compositional generalization, analyzing its characteristics across four dimensions: systematicity, compositionality, interpretability, and dynamic adaptability. A classification system for compositional generalization methods based on architectural evolution is then proposed, in which existing compositional methods are categorized according to their foundational representation architecture, relational modeling architecture, and learning mechanism architecture. The mechanism linking each method’s model structure to compositional generalization is elaborated in detail. The advantages and disadvantages, as well as the scene applicability of different methods, are further compared across various scenarios. Finally, feasible solutions are proposed to address persistent challenges in compositional generalization, such as dynamics, knowledge dependence, computational complexity, and heterogeneous relationships, thereby providing guidance for in-depth research in this field.
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备注/Memo
收稿日期:2025-7-1。
基金项目:国家自然科学基金项目（61806221）.
作者简介:李昌原，硕士研究生，主要研究方向为组合泛化。E-mail：1744014975@qq.com。;程恺，副教授，主要研究方向为智能任务规划、数据分析挖掘。获军队科技进步奖二等奖2项、三等奖3项、指控学会二等奖1项。发表学术论文60余篇，出版学术专著2部。E-mail：chengkai911@126.com。;郝文宁，教授，主要研究方向为大规模高维数据压缩与作战效能评估。发表国际期刊论文20余篇。E-mail：hwnbox@aeu.edu.cn。
通讯作者:程恺. E-mail：chengkai911@126.com
更新日期/Last Update:
1900-01-01