Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate between connectionist and classicist, wherein the latter continually advocates an algebraic treatment in cognitive architectures. In this work, we follow the classicist's call and propose a hybrid approach to improve systematic generalization in reasoning. Specifically, we showcase a prototype with algebraic representation for the abstract spatial-temporal reasoning task of Raven's Progressive Matrices (RPM) and present the ALgebra-Aware Neuro-Semi-Symbolic (ALANS) learner. The ALANS learner is motivated by abstract algebra and the representation theory. It consists of a neural visual perception frontend and an algebraic abstract reasoning backend: the frontend summarizes the visual information from object-based representation, while the backend transforms it into an algebraic structure and induces the hidden operator on the fly. The induced operator is later executed to predict the answer's representation, and the choice most similar to the prediction is selected as the solution. Extensive experiments show that by incorporating an algebraic treatment, the ALANS learner outperforms various pure connectionist models in domains requiring systematic generalization. We further show that the algebraic representation learned can be decoded by isomorphism to generate an answer.
翻译:情报是由联系主义者或经典主义者实现的吗?尽管联系主义者的方法已经实现了超人性的表现?尽管联系主义者的方法已经实现了超人性的表现,但越来越多的证据表明,这种特定任务的优越性在系统性的概括化中特别脆弱。这种观察存在于联系主义者和经典主义者之间的中心辩论之中,后者不断主张认知结构中的代数处理。在这项工作中,我们遵循经典主义者的号召,并提议一种混合方法来改进理性的系统化概括化。具体地说,我们展示了一个具有代数代表的原型,用于雷文进步马斯特(RPM)的抽象空间-时空推理任务,并正在介绍ALgebra-Aware Neuro-Semi-Symbolic(ALNS)的学习者。ALANS 学习者受到抽象的代数和代言理学理论的驱动力。我们遵循的是经典视觉直观感的预言, 我们的后端将它转换成一个隐蔽的操作者。我们后来执行的操作者将预测一个系统化的代数的代言的代言式, 将显示一个选择的系统化的代言式的代言式的代言。我们可以显示一个选择的系统化的代言式的代言式。