让智能体在线构建可解释的因果模型，自动发现有用概念并持续优化。
Continual learning and refinement of causal models through dynamic predicate invention
- 用元解释学习和谓词发明，在线生成符号化因果模型。
- 在复杂关系场景中，样本效率比传统方法高出数个数量级。
- 适合需要可解释性与长期学习能力的研究者或应用。
高效应对复杂环境需要智能体内化世界底层逻辑，但传统世界建模方法常面临样本效率低、透明度差和可扩展性不足的问题。我们提出一种框架，通过将连续模型学习与修复整合进智能体决策循环，实现完全在线的符号化因果世界模型构建。该框架利用元解释学习和谓词发明，发现语义有意义且可复用的抽象，使智能体能从观测中构建层次化、解耦的高质量概念。实验表明，我们的提升推理方法可扩展至具有复杂关系动态的领域，而命题方法在此类场景中会遭遇组合爆炸；同时，在样本效率上相比成熟的基于PPO的神经网络基线高出数个数量级。
原文摘要 · Abstract (English)
Efficiently navigating complex environments requires agents to internalize the underlying logic of their world, yet standard world modelling methods often struggle with sample inefficiency, lack of transparency, and poor scalability. We propose a framework for constructing symbolic causal world models entirely online by integrating continuous model learning and repair into the agent's decision loop, by leveraging the power of Meta-Interpretive Learning and predicate invention to find semantically meaningful and reusable abstractions, allowing an agent to construct a hierarchy of disentangled, high-quality concepts from its observations. We demonstrate that our lifted inference approach scales to domains with complex relational dynamics, where propositional methods suffer from combinatorial explosion, while achieving sample-efficiency orders of magnitude higher than the established PPO neural-network-based baseline.
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