论文标题
类比为非参数贝叶斯对关系系统的推断
Analogy as Nonparametric Bayesian Inference over Relational Systems
论文作者
论文摘要
人类的学习和推理可以构成关系概括的计算问题。在这个项目中，我们提出了一个贝叶斯模型，该模型通过从先前遇到的关系结构中进行类似加权预测来概括到新的环境中。首先，我们表明，当对环境的经验很小时，该学习者对基于随机和Wikipedia的系统的关系数据胜过基于理论的学习者。接下来，我们展示我们对类比相似性的形式化如何转化为类比的选择和加权。最后，我们将基于类比和理论的学习者结合在单个非参数贝叶斯模型中，并表明，从依靠类比到建立具有越来越多的经验的新型系统的理论。除了预测未观察到的相互作用比任何一个基线都更好，此形式化还提供了对类比本身的形成和抽象的计算级别的观点。
Much of human learning and inference can be framed within the computational problem of relational generalization. In this project, we propose a Bayesian model that generalizes relational knowledge to novel environments by analogically weighting predictions from previously encountered relational structures. First, we show that this learner outperforms a naive, theory-based learner on relational data derived from random- and Wikipedia-based systems when experience with the environment is small. Next, we show how our formalization of analogical similarity translates to the selection and weighting of analogies. Finally, we combine the analogy- and theory-based learners in a single nonparametric Bayesian model, and show that optimal relational generalization transitions from relying on analogies to building a theory of the novel system with increasing experience in it. Beyond predicting unobserved interactions better than either baseline, this formalization gives a computational-level perspective on the formation and abstraction of analogies themselves.
