SGER: Building Blocks for Creative Search

SGER：创意搜索的构建模块

• 批准号: 0733581
• 负责人: Andrew Barto
• 金额: --
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2009-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0733581&HistoricalAwards=false
• 关键词: SGER; Building; Blocks; Creative; Search

This project will develop a formal framework based on optimization and reinforcement learning to model important features of creative processes. Large, ill-defined optimization problems that characterize situations where creativity comes into play require selectional, or generate-and-test, procedures that include both a smart generator and a smart tester. The generator responsible for generating structures to be evaluated should be able to generate structures that are novel while at the same time have high probability of being successful. This project investigates new methods for injecting structured, knowledge-based, novelty into the generation process. The tester, the process that evaluates alternatives, should be a good surrogate for the primary objective function, which is often not easily or inexpensively accessible. A smart tester uses a combination of a priori knowledge, knowledge accumulated from past creative activity, and information gained during the current creative activity to assess alternatives. The working hypothesis is that the synergy created by the interaction of a sufficiently smart generator and a sufficiently smart tester can account for important aspects of creative processes.Intellectual Merit. Although there have been past attempts to mathematically and computationally model aspects of creativity, few bring to bear modern developments in machine learning or take advantage of recent advances in computational reinforcement learning and its relation to animal reward and motivational systems. Furthermore, computational studies have not taken advantage of psychological theories of play, curiosity, surprise, and other factors involved in intrinsically motivated behavior and that perform significant roles in creative activities. This project addresses these shortcomings by taking a interdisciplinary approach. The project will meet the challenge of providing a coherent theoretical account of aspects of creativity without losing sight of the fluidity and flexibility of creative processes.Broader Impacts. Representing key elements of creative processes in a mathematically coherent framework can stimulate new directions of research in computer science, engineering, design research, and psychology. Algorithms designed according to this framework can facilitate the design of creative artificial agents as well as form the basis of tools for enhancing human creativity and creative enterprises. Such a framework can also provide a principled means for comparing performances of algorithms purporting to show creativity, thus forming a component of future research methodology directed toward creativity. The project has the potential to contribute to our understanding of general principles underlying human creativity, with implications for design, education, and the arts.

该项目将开发一个基于优化和强化学习的正式框架，以模拟创造性过程的重要特征。大型的、定义不明确的优化问题，其特征在于创造力发挥作用的情况，需要选择性的或生成和测试的过程，包括智能生成器和智能测试器。负责生成待评估结构的生成器应该能够生成新颖的结构，同时具有高的成功概率。本项目研究了将结构化的、以知识为基础的、新奇注入生成过程的新方法。测试者，评估备选方案的过程，应该是主要目标函数的一个很好的替代品，这通常是不容易或不便宜的。一个聪明的测试者使用先验知识，从过去的创造性活动中积累的知识，以及在当前创造性活动中获得的信息来评估替代品。工作的假设是，一个足够聪明的发电机和一个足够聪明的测试器的相互作用所产生的协同作用可以解释创造性过程的重要方面。尽管过去曾尝试对创造力的各个方面进行数学和计算建模，但很少有人能够利用机器学习的现代发展，或者利用计算强化学习的最新进展及其与动物奖励和激励系统的关系。此外，计算研究还没有利用心理学理论的发挥，好奇心，惊喜，和其他因素参与内在动机的行为，并在创造性活动中发挥重要作用。该项目通过采取跨学科方法解决这些缺点。该项目将迎接挑战，为创意的各个方面提供连贯的理论解释，同时又不忽视创意过程的流动性和灵活性。在数学连贯的框架中表示创造性过程的关键元素可以激发计算机科学，工程，设计研究和心理学的新研究方向。根据这个框架设计的算法可以促进创造性人工智能体的设计，以及形成的基础上，提高人类的创造力和创造性企业的工具。这样的框架还可以提供一个原则性的手段，用于比较旨在显示创造力的算法的性能，从而形成面向创造力的未来研究方法的一个组成部分。该项目有可能有助于我们理解人类创造力的基本原理，并对设计，教育和艺术产生影响。