RI: Small: Neuroevolution of Brain-Inspired Computational Models Over Vast Timescales

RI：小：大脑启发计算模型在广阔时间尺度上的神经进化

• 批准号: 1421925
• 负责人: Kenneth Stanley
• 金额: $ 47.25万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421925&HistoricalAwards=false
• 关键词: RI; Small; Neuroevolution; Brain; Inspired

For many years researchers inspired by the idea of natural selection have experimented with computer programs called evolutionary algorithms. These algorithms simulate a kind of artificial breeding process in which a set of candidates generated by the computer are evaluated for their ability to perform a desired task. The best performers are then allowed to reproduce with slight variation to form a new and hopefully improved generation. In recent years evolutionary algorithms have exhibited the ability to evolve brain-like structures called artificial neural networks in an approach called neuroevolution. These evolved networks perform tasks often critical to technological progress and artificial intelligence like controlling robots or recognizing images. However, unlike evolution in nature, which yields dramatic changes over hundreds of thousands of generations, evolutionary algorithms have rarely been run for more than a few thousand. This project for the first time is applying new evolutionary techniques that reward continual novelty and diversification to experiments evolving over hundreds of thousands of generations, on the scale of nature. The driving hypothesis is that modern evolutionary algorithms run on this scale can yield robotic behaviors, agent morphologies, and decision-making capabilities significantly beyond the current state of the art.To investigate long-term evolution in practice, artificial neural networks are being evolved in a variety of domains through new kinds of novelty-driven neuroevolution algorithms designed to avoid the convergence seen in typical evolutionary experiments. Because this new class of algorithms tends to avoid convergence, the long-term dynamics and ultimate potential for discovery of such algorithms over vast time scales (i.e. hundreds of thousands of generations) is almost entirely unknown. The idea of running neuroevolution at unprecedented timescales mirrors recent results in related areas like deep learning where massive computation has proven capable of fundamentally altering the kinds of problems that can be solved. Because evolutionary runs over hundreds of thousands of generations yield enormous troves of evolutionary data, an important component of the project is the development visualization techniques for exploring and characterizing the results of such runs. The project overall is producing a new set of tools, a new set of evolved capabilities for autonomous control and decision-making, and an increased understanding of the implications of big data and big computation for simulated evolution in computers.

多年来，受自然选择思想的启发的研究人员已经尝试了称为进化算法的计算机程序。 这些算法模拟了一种人工育种过程，其中评估了计算机生成的一组候选者，以执行所需的任务。 然后允许表现最好的人以微小的变化复制，以形成新的且希望得到改善的一代。 近年来，进化算法已经表现出以一种称为神经进化的方法来发展称为人工神经网络的脑样结构的能力。 这些进化的网络执行通常对技术进步和人工智能至关重要的任务，例如控制机器人或识别图像。 但是，与自然界中的进化不同，它在数十万世代中产生了巨大变化，进化算法很少运行超过几千。 该项目首次采用新的进化技术，这些技术将持续的新颖性和多样化奖励到具有自然规模的实验中。 驾驶假设是，以这种规模运行的现代进化算法可以产生机器人行为，代理形态和决策能力，大大超出了艺术的现状。为了研究实践中的长期进化，人工神经网络正在通过新型的新型神经进化量进行的各种领域在各种领域中进化，以避免使用新型的神经进化，以避免使用典型的构成典型的构成典型的构成典范。 因为这种新的算法倾向于避免融合，所以在庞大的时间尺度上发现这种算法的长期动态和最终潜力（即数十万代）几乎完全未知。 在前所未有的时间标准上运行神经进化的想法反映了相关领域的最新结果，例如深度学习，大规模计算已被证明能够从根本上改变可以解决的问题。 由于进化的运行超过数十万代产生了巨大的进化数据，因此该项目的重要组成部分是用于探索和表征此类运行结果的开发可视化技术。 该项目总体正在生产一套新的工具，这是一组新的发展功能，用于自主控制和决策，以及对大数据和大量计算对计算机中模拟演变的含义的越来越多的理解。