权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

学习发展洞察力：学习迁移的子符号方法

基本信息

批准号：
0840004
负责人：
Dan Ventura
金额：
$ 10.37万
依托单位：
Brigham Young University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-01 至 2011-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0840004&HistoricalAwards=false
关键词：
Learning Develop Insight Sub symbolic

项目摘要

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.

创造性的问题解决缺乏超出预期类比或学习迁移的计算模型。许多确实存在的计算模型都利用了它们所展示的领域的语义。该项目考虑了创造性问题解决计算模型的子符号方法。子符号系统中创造性问题解决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，并将为创造力的几个方面：类比、重新表示和洞察力提供有根据的（子符号）计算解释。为了探索和展示创造力的子符号方法，该项目实现了一个用于学习数据流形维度固有映射的系统，并将实现一个基于地标的元学习系统，用于知识转移机制和知识表示。该项目将针对一系列问题对系统进行分析和实证验证。更广泛的影响：拟议的研究将使系统更有能力创造性地、像人类一样解决问题，从而造福社会；并将通过让本科生和研究生参与研究活动以及为研究生提供教学和指导机会，将教育与研究结合起来。