Learning to Develop Insight: A Sub-symbolic Approach to Learning Transfer

创造性地解决问题缺乏超越预期的类比或学习迁移的计算模型。许多已经存在的计算模型都利用了演示它们所在领域的语义。这个项目考虑了一种亚符号的方法来解决创造性问题的计算模型。在子符号系统中创造性解决问题的发展需要在知识表示、元学习和知识转移机制方面进行创新研究，并将对创造力的几个方面（类比、再表示和洞察力）进行有基础的（子符号）计算解释。为了探索和展示创造力的子符号方法，该项目实现了一个系统，用于学习数据流形维度中固有的映射，并将实现一个基于里程碑的元学习系统，用于知识转移机制和知识表示。该项目将在一系列问题上分析和实证验证系统。更广泛的影响：拟议的研究将使系统更有创造性，更像人类解决问题的能力，从而造福社会；通过让本科生和研究生参与研究活动，并为研究生提供教学和指导机会，将教育与研究结合起来。

Creative problem solving lacks computational models that go beyond expected analogies or learning transfer. Many of the computational models that do exist exploit the semantics of the domain in which they are demonstrated. This project considers a sub-symbolic approach to a computational model of creative problem solving. The development of creative problem solving in sub-symbolic systems requires innovative research in knowledge representation, meta-learning and knowledge transfer mechanisms and will result in a well-grounded (sub-symbolic) computational explanation for several aspects of creativity: analogy, re-representation and insight. To explore and demonstrate a sub-symbolic approach to creativity, the project implements a system for learning the mappings inherent in the dimensions of a data manifold, and will implement a landmark-based meta-learning system for knowledge transfer mechanisms and knowledge representation. The project will analyze and empirically validate the system on a suite of problems. Broader Impacts: The proposed research will benefit society by making systems more capable of creative, human-like problem solving; and will integrate education with research by involving undergraduate and graduate students in research activities and by providing teaching and mentoring opportunities for the graduate students.