SGER: Building Blocks for Creative Search

这个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、定义不清的优化问题，其特点是创造力发挥作用的情况，需要包括智能生成器和智能测试器的选择或生成和测试过程。负责生成待评估结构的生成器应该能够生成新颖的结构，同时具有很高的成功概率。该项目研究了在生成过程中注入结构化的、基于知识的新颖性的新方法。测试者，评估备选方案的过程，应该是主要目标函数的很好的替代品，而主要目标函数通常不容易或不昂贵地访问。聪明的测试员使用先验知识、从过去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备选方案。工作假设是，一个足够智能的发电机和一个足够智能的测试器相互作用所产生的协同效应可以解释创造性过程的重要方面。尽管过去有人尝试对创造力的各个方面进行数学和计算建模，但很少有人利用机器学习的现代发展，或者利用计算强化学习及其与动物奖励和动机系统的关系的最新进展。此外，计算研究没有利用游戏、好奇心、惊讶等心理学理论，这些因素涉及内在动机行为和在创造性活动中发挥重要作用的其他因素。这个项目通过采取跨学科的方法来解决这些缺点。该项目将迎接这样的挑战，即在不忽视创造性过程的流动性和灵活性的情况下，对创造力的各个方面提供连贯的理论描述。在一个数学连贯的框架中表示创造过程的关键元素可以刺激计算机科学、工程学、设计研究和心理学的新研究方向。根据该框架设计的算法可以促进创造性人工智能体的设计，并形成提高人类创造力和创造性企业的工具的基础。这样的框架还可以提供一种原则性的手段，用于比较声称展示创造力的算法的性能，从而形成未来针对创造力的研究方法的组成部分。该项目有可能有助于我们理解人类创造力背后的一般原则，并对设计、教育和艺术产生影响。

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.