Pilot: Leveraging Human Creativity with Machine Discovery

试点：通过机器发现利用人类创造力

• 批准号: 0757479
• 负责人: Risto Miikkulainen
• 金额: $ 20万
• 依托单位: University of Texas at Austin
• 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: 2008-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0757479&HistoricalAwards=false
• 关键词: Pilot; Leveraging; Human; Creativity; Machine

A challenge in machine learning is to devise methods that allow incorporating human insight into the automated learning process. Current learning methods employ representations that make it difficult to encode simplification and specific examples, and learning is based on random exploration that is difficult to direct. NEAT is a learning system where the learned decision policy is represented in neural networks and learned through evolutionary optimization, i.e. genetic algorithms. NEAT evolves network structure as well as weights, which makes it possible in principle to incorporate human guidance in three ways: (1) building a gradually more complex network structure through shaping from simple to more complex tasks, (2) training networks with examples of human behavior, and (3) converting human-designed rules into network structures. These techniques will be developed and evaluated in the domain of designing complex behaviors for autonomous agents in the NERO 3D simulation environment. In a series of human subject experiments, the solutions designed through human-guided neuroevolution will be compared to those designed by human engineers and to those discovered by neuroevolution alone, verifying that (a) the human-guided approach results in better solutions, and (b) those solutions are more creative. The result of this project is a machine learning approach will allow engineers to generate creative designs to many real-world sequential decision problems. Applications of this approach will lead to safer and more efficient vehicle, traffic, and robotic control, improved process and manufacturing optimization, and more efficient computer and communication systems. It will also make the next generation of video games possible, with characters that exhibit realistic and adaptive behaviors; such technology should lead to more effective educational and training games in the future.

机器学习的挑战是设计方法，使人类洞察力融入自动化学习过程。当前的学习方法采用表示难以编码简化和特定示例的表示形式，并且学习是基于难以指导的随机探索。整洁是一种学习系统，在该系统中，学到的决策政策在神经网络中表示，并通过进化优化（即遗传算法）学习。整洁的人会演变网络结构和权重，这原则上可以通过三种方式纳入人类的指导：（1）通过从简单到更复杂的任务构建逐渐复杂的网络结构，（2）培训人类行为示例的培训网络，（3）将人类设计的人类设计的规则转化为网络结构。这些技术将在Nero 3D仿真环境中为自主剂设计复杂行为的领域开发和评估。在一系列人类主题实验中，将通过人类引导的神经进化设计的解决方案与人类工程师和单独神经进化的人进行比较，并验证（a）（a）人类引导的方法会带来更好的解决方案，并且（b）这些解决方案更具创造力。该项目的结果是一种机器学习方法将使工程师能够为许多真实世界的顺序决策问题生成创意设计。这种方法的应用将导致更安全，更高效的车辆，交通和机器人控制，改进的过程和制造优化以及更有效的计算机和通信系统。这也将使下一代视频游戏成为可能，具有表现出现实和适应性行为的角色；这样的技术将来应该导致更有效的教育和培训游戏。