受生物发育启发，构建可持续学习、自主规划与行为可解释的智能系统。
Foundations of a Developmental Design Paradigm for Integrated Continual Learning, Deliberative Behavior, and Comprehensibility
- 基于进化发育生物学原理，设计无需梯度更新的持续学习机制。
- 在简单环境与MNIST数据上实现结构自适应与分层行为分解。
- 适合研究通用人工智能、可解释性系统与长期学习框架的开发者。
当前机器学习系统在持续学习、信息复用、可解释性及与自主行为整合方面存在固有局限，日益受到关注。为解决这些问题，我们提出一种新系统设计，其核心学习方法受进化发育生物学原理启发，克服了现有方法的关键缺陷。该设计包含三个核心组件：模组器（Modeller），一种无需梯度的持续学习与结构自适应机制；规划器，用于对已学模型进行目标导向行动；行为封装机制，可将复杂行为分解为层级结构。我们在一个简单测试环境中验证了原理可行性，并将建模框架扩展至高维网络空间，使用MNIST数据集完成形状检测任务。结果表明，该框架能有机地同时克服多个主流机器学习系统的重大局限。
原文摘要 · Abstract (English)
Inherent limitations of contemporary machine learning systems in crucial areas -- importantly in continual learning, information reuse, comprehensibility, and integration with deliberate behavior -- are receiving increasing attention. To address these challenges, we introduce a system design, fueled by a novel learning approach conceptually grounded in principles of evolutionary developmental biology, that overcomes key limitations of current methods. Our design comprises three core components: The Modeller, a gradient-free learning mechanism inherently capable of continual learning and structural adaptation; a planner for goal-directed action over learned models; and a behavior encapsulation mechanism that can decompose complex behaviors into a hierarchical structure. We demonstrate proof-of-principle operation in a simple test environment. Additionally, we extend our modeling framework to higher-dimensional network-structured spaces, using MNIST for a shape detection task. Our framework shows promise in overcoming multiple major limitations of contemporary machine learning systems simultaneously and in an organic manner.
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