Pilot: Leveraging Human Creativity with Machine Discovery

机器学习的一个挑战是设计出一种方法，允许将人类的洞察力融入自动学习过程。目前的学习方法采用的表示方法很难对简化和具体示例进行编码，并且学习是基于难以指导的随机探索。NEAT是一个学习系统，其中学习的决策策略在神经网络中表示，并通过进化优化（即遗传算法）学习。NEAT进化了网络结构和权重，原则上可以通过三种方式引入人类指导：（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）用人类行为的例子训练网络，（3）将人类设计的规则转换为网络结构。这些技术将在NERO 3D仿真环境中为自主代理设计复杂行为的领域中进行开发和评估。在一系列人类实验中，通过人类指导的神经进化设计的解决方案将与人类工程师设计的解决方案以及仅通过神经进化发现的解决方案进行比较，以验证（a）人类指导的方法产生更好的解决方案，（B）这些解决方案更具创造性。该项目的结果是机器学习方法将允许工程师为许多现实世界的顺序决策问题生成创造性的设计。这种方法的应用将导致更安全和更有效的车辆，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。它还将使下一代视频游戏成为可能，其角色表现出逼真和适应性的行为;这种技术应该会在未来带来更有效的教育和培训游戏。

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.