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

合作研究：NSF2026：EAGER：发展 AGI 的游乐场和提案。

基本信息

批准号：
2033938
负责人：
Joshua Hartshorne
金额：
$ 19.87万
依托单位：
Boston College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033938&HistoricalAwards=false
关键词：
Collaborative Research NSF2026 EAGER Playground

项目摘要

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.

在智能和信息系统部（IIS）和NSF 2026基金计划中的强大情报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Brandeis大学的调查人员正在解决通过与环境互动以及与环境的互动以及对环境的互动以及对环境的互动来综合象征性或逻辑推理来创造人工通用情报的挑战。受到自然（例如人类）智力的结构的启发，由此产生的心理体系结构将这些策略都用于他们在（“所有世界的最佳世界”或“ BAW，方法”中的问题）中脱颖而出。该项目的成功完成将促进AI和心理/神经科学领域的一系列研究项目。长期，预计AGI的发展将通过使计算机能够开发出基于世界经验的抽象概念，并产生新颖的思想和事件，从而对社会产生重大利益。该项目还将有助于扩大学生的培训和妇女和人为少数群体的参与。该项目旨在原型一种新的建筑，并针对开放式任务进行测试，该任务对人工智能而言很难，但到处都是人工幼儿：揭开块，容器和其他小物体的负担。主要目的是建立一个模拟婴儿可以探索，操纵和学习的虚拟世界；建立一个模拟婴儿的工作原型，编码BAW心理架构的关键方面；并评估代理商在几个困难的开放式任务上的性能。这种建筑促进了从自然智能研究中的关键概念的清单，这些概念很少在人工智能中使用：心理模型，探索性游戏和分支。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估而被认为是珍贵的支持。