Collaborative Research: NSF2026: EAGER: A Playground and Proposal for Growing an AGI

在智能和信息系统（IIS）部门的鲁棒智能计划和综合活动办公室的NSF 2026基金计划的支持下，波士顿学院和布兰代斯大学的研究人员正在通过合成符号或逻辑推理，通过与环境的交互进行学习，以及最先进的神经网络来解决创建人工通用智能的挑战。灵感来自自然结构（例如，人类）智能，由此产生的心理架构部署这些策略中的每一个，他们擅长的问题（“最好的所有世界”，或BAW，方法）。该项目的成功完成将促进人工智能和心理学/神经科学的一系列研究项目。从长远来看，AGI的发展预计将对社会产生重大利益，使计算机能够开发基于世界经验的抽象概念，并产生新颖的想法和发明。该项目还将有助于扩大学生培训和妇女和代表性不足的少数民族的参与。该项目旨在建立一个新的架构原型，并将其与一项开放式任务进行测试，该任务对人工智能来说很难，但世界各地的人类幼儿都掌握：揭示块，容器和其他小物体的启示。主要目的是建立一个虚拟世界，一个模拟的婴儿可以探索，操纵和学习;建立一个工作原型的模拟婴儿纳入关键方面的BAW的心理架构;和评估代理的性能在几个困难的，开放式的任务。该架构有助于将自然智能研究中不常用于人工智能的关键概念纳入其中：心智模型，探索性游戏和组块。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Robust Intelligence program in the Division of Intelligent and Information Systems (IIS) and the NSF 2026 Fund Program in the Office of Integrated Activities, investigators at Boston College and Brandeis University are addressing the challenge of creating Artificial General Intelligence by synthesizing symbolic or logical reasoning, learning through interaction with the environment, as well as state-of-the-art neural networks. Inspired by the structure of natural (e.g., human) intelligence, the resulting mental architecture deploys each of these strategies for the problems they excel at (the "Best of All Worlds”, or BAW, approach). Successful completion of this project will facilitate a range of research projects in AI and psychology/neuroscience. Long-term, the development of AGI is expected to have significant benefits to society, by enabling computers to develop abstract concepts grounded in experience with the world, and to generate novel ideas and inventions. This project will also help broaden student training and participation of women and underrepresented minorities.This project aims to prototype a new architecture and test it against an open-ended task that is difficult for artificial intelligence but mastered by human toddlers everywhere: uncovering the affordances of blocks, containers, and other small objects. The primary aims are to build a virtual world that a simulated infant can explore, manipulate, and learn from; build a working prototype of a simulated infant incorporating key aspects of the BAW mental architecture; and evaluate the performance of the agent on several difficult, open-ended tasks. This architecture facilitates incorporation of key concepts from the study of natural intelligence that are infrequently used in artificial intelligence: mental models, exploratory play, and chunking.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.