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.
