用逻辑编程建模物体行为，让模型更准、更可解释。
Object-Oriented Transition Modeling with Inductive Logic Programming
- 基于归纳逻辑编程构建物体动态模型
- 在新场景下表现优于现有方法，泛化能力更强
- 适合需要可解释性的智能系统研究者
从观测中构建世界模型，即归纳学习，是机器学习的核心挑战之一。为具备实用性，模型需在新情境中保持准确性，即具备泛化能力，同时应易于解释且训练高效。以往工作已在受人类认知启发的物体表示框架下探索这些概念。本文提出一种全新学习算法，显著优于此前方法。通过全面实验，包括消融测试与神经基线对比，验证了其在性能上的显著提升。
原文摘要 · Abstract (English)
Building models of the world from observation, i.e., induction, is one of the major challenges in machine learning. In order to be useful, models need to maintain accuracy when used in novel situations, i.e., generalize. In addition, they should be easy to interpret and efficient to train. Prior work has investigated these concepts in the context of object-oriented representations inspired by human cognition. In this paper, we develop a novel learning algorithm that is substantially more powerful than these previous methods. Our thorough experiments, including ablation tests and comparison with neural baselines, demonstrate a significant improvement over the state-of-the-art.
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