论文标题
学习和扎根的代数方法
An Algebraic Approach to Learning and Grounding
论文作者
论文摘要
我们考虑从示例中学习复合代数表达式语义的问题。结果是一种用于研究学习任务的多功能框架，可以放入以下抽象形式中：输入是部分代数$ \ alg $和一组有限的示例$（φ_1，o_1），（φ_2，o_2，o_2），\ ldots $，每个由algebraic term $ $ $φ_i$ $φ_i$ cobsocs $ ucoction $ ucogect和ucy $φ_i$φ目的是同时填写$ \ alg $中的缺失代数操作，并将每个$φ_i$的变量填充$ o_i $中的每个变量，以便优化条款的合并价值。我们通过案例研究在语法推理，图像学习和逻辑场景描述的基础中证明了该框架的适用性。
We consider the problem of learning the semantics of composite algebraic expressions from examples. The outcome is a versatile framework for studying learning tasks that can be put into the following abstract form: The input is a partial algebra $\alg$ and a finite set of examples $(φ_1, O_1), (φ_2, O_2), \ldots$, each consisting of an algebraic term $φ_i$ and a set of objects~$O_i$. The objective is to simultaneously fill in the missing algebraic operations in $\alg$ and ground the variables of every $φ_i$ in $O_i$, so that the combined value of the terms is optimised. We demonstrate the applicability of this framework through case studies in grammatical inference, picture-language learning, and the grounding of logic scene descriptions.
