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.

翻译：从观测数据中构建世界模型（即归纳）是机器学习面临的主要挑战之一。为具备实用性，模型需在新情境中保持准确性（即泛化能力），同时应易于解释且训练高效。先前研究已在受人类认知启发的面向对象表示框架下探讨了这些概念。本文提出了一种全新的学习算法，其性能显著优于现有方法。通过包括消融实验及与神经基线模型对比在内的全面实验验证，本方法在现有技术水平上实现了显著提升。