EAGER: NSF2026: From Thinking to Inventing: Towards Creative Agents that Discover Novelty and Learn how to Accommodate it

虽然最近的成功证明了人工智能（AI）技术的潜力，但在人工智能程序能够达到人类思维的认知灵活性和复杂性之前，还有一些顽强的挑战需要解决。最紧迫的问题之一是如何处理未知的、新颖的情境或AI系统最初没有设计的世界方面。这种“开放世界”AI仍然处于起步阶段，现有的AI技术并不容易转移到开放世界。该项目的目标是研究人工智能代理如何被赋予创造性的解决问题的技能，使他们能够在开放的世界中谈判，并发明新的工具、概念和最终的理论。这样的系统可能是人工智能领域的下一个重大颠覆性技术，它不仅能让机器人在遇到故障和意外事件时具有长期的自主性和弹性，更重要的是，它提供的技术可以加速解决人类目前只能缓慢解决的紧迫问题。目前的人工智能算法依赖于拥有完整的任务模型和它们应该运行的领域。如果信息缺失，他们就无法计划如何获取信息和扩展知识。然而，能够确定他们缺少关键知识的系统可以使用这些信息来指导他们的知识获取过程，并可能开发出发明新工具和理论的创造性方法。该项目将开发一个集成的问题解决系统，该系统将适应和整合不同的学习技术，并根据方法最适合解决的问题方面，以有针对性的方式部署每种方法。这个系统将被扩展为能够应用不同的策略来实验（就像人类一样）环境中的物体，以发现它们的属性和可以用这些物体执行的动作，从而建立关于物体及其功能的新知识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

While recent successes demonstrate the potential of artificial intelligence (AI) technologies, there are tenacious challenges left to address before AI programs will be able to reach the cognitive flexibility and sophistication of the human mind. One of the most pressing problems is how to deal with the unknown, with novel contexts or aspects of the world for which an AI system was not originally designed. Such "open-world" AI is still in its infancy and existing AI techniques do not easily transfer to open worlds. The goal of this project is to investigate ways in which AI agents can be endowed with creative problem-solving skills that allow them to negotiate open worlds and invent new tools, concepts, and eventually theories. Such a system could be the next major disruptive technology in AI, enabling not only long-term autonomy and resilience of robots in the light of faults and unexpected events, but more importantly, providing technology that could accelerate the solution of pressing problems that humanity is currently able to solve only slowly, if at all.Current AI algorithms rely on having complete models of the task and domain in which they are supposed to operate. If information is missing, they are not able to plan how to acquire it and extend their knowledge. Yet, systems that can determine that they are missing critical knowledge could use that information to guide their knowledge acquisition process and possibly develop creative approaches for inventing new tools and theories. This project will develop an integrated problem-solving system that adapts and incorporates different learning techniques and deploys each of these methods in a targeted fashion based on the problem aspect the approach is best equipped to solve. This system will be extended with the ability to apply different strategies for experimenting (just like humans) with objects in its environment to discover their properties and the actions that can be performed with those objects to build up new knowledge about objects and their functions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.