CAREER: Creating Robust, Adaptable Computational Intelligence by Recreating Key Properties of Animal Brains

职业：通过重建动物大脑的关键特性来创建强大的、适应性强的计算智能

• 批准号: 1453549
• 负责人: Jeff Clune
• 金额: $ 50.75万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453549&HistoricalAwards=false
• 关键词: CAREER; Creating; Robust; Adaptable; Computational

Natural animals display awe-inspiring adaptability and robustness (e.g. three-legged dogs that can still run and catch frisbees). One reason neuroscientists believe animals are so capable is because their brains are structurally organized in that they exhibit neural regularity, modularity, and hierarchy. This project will test whether those neural properties also improve the robustness, adaptability, and overall intelligence of evolved computational agents, specifically robots. Evolutionary algorithms (EAs) evolve, rather than engineer, computational agents to produce qualities seen in nature, such as robustness and adaptability. While EAs often outperform human engineers, the robot bodies and brains they design pale in comparison to those of natural animals. Recent advances by the PI (Professor Clune) and others enable the evolution of neural networks (computational brains) that exhibit regularity, modularity, and hierarchy.Professor Clune and his students will test whether regularity, modularity, and hierarchy, separately and in combination, improve (1) robustness to noise (2) robustness to damage (3) adaptability to new environments (4) learning, and (5) overall intelligence, measured as the ability to solve challenges of varying complexities. Based on his previous work and preliminary results, Clune anticipates that neural regularity, modularity, and hierarchy could increase all of these desirable behavioral qualities. Such knowledge will accelerate humanity's ability to deploy effective, autonomous robots, which will provide tremendous benefits to society (e.g. search and rescue, putting out fires, and elderly care). The research is woven into a tightly integrated educational plan that generates broader impacts via a robotics club in Laramie, a research-oriented course in evolutionary robotics, public education via videos and the press, and broadening participation in graduate training.

自然动物表现出令人惊叹的适应性和鲁棒性（例如仍然可以奔跑和捕捉飞盘的三足狗）。神经科学家相信动物如此有能力的原因之一是因为它们的大脑在结构上组织得很好，表现出神经规律性、模块化和层次性。该项目将测试这些神经特性是否也能提高进化计算代理（特别是机器人）的鲁棒性、适应性和整体智能。 进化算法（EA）通过进化而不是设计计算代理来产生自然界中可见的品质，例如鲁棒性和适应性。虽然 EA 的表现常常优于人类工程师，但他们设计的机器人身体和大脑与自然动物相比显得苍白无力。 PI（Clune 教授）和其他人的最新进展使得神经网络（计算大脑）的进化呈现出规律性、模块化和层次性。Clune 教授和他的学生将测试规律性、模块化和层次性是否单独或组合提高（1）对噪声的鲁棒性（2）对损坏的鲁棒性（3）对新环境的适应性（4）学习和（5）整体 智力，衡量的是解决不同复杂性挑战的能力。根据他之前的工作和初步结果，Clune 预计神经规律性、模块化和层次结构可以提高所有这些理想的行为品质。这些知识将加速人类部署有效的自主机器人的能力，这将为社会带来巨大的好处（例如搜索和救援、灭火和老年护理）。 该研究融入了一个紧密结合的教育计划，通过拉勒米的机器人俱乐部、进化机器人学研究型课程、通过视频和媒体进行公共教育以及扩大研究生培训的参与，产生更广泛的影响。