Pilot: Leveraging Human Creativity with Machine Discovery

机器学习的一个挑战是设计方法，允许将人类的洞察力融入到自动学习过程中。目前的学习方法使用表示法，这使得对简化和特定示例进行编码变得困难，并且学习基于难以指导的随机探索。Neat是一种学习系统，其中学习的决策策略用神经网络表示，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，这使得在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塑造逐渐更复杂的网络结构，(2)用人类行为的例子训练网络，以及(3)将人类设计的规则转换为网络结构。这些技术将在Nero 3D仿真环境中自主代理的复杂行为设计领域进行开发和评估。在一系列人体实验中，通过人类引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)人类引导的方法会产生更好的解决方案，以及(B)这些解决方案更具创造性。该项目的结果是一种机器学习方法，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高效的车辆、交通和机器人控制，改进的工艺和制造优化，以及更高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，游戏中的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.