Collaborative Research: CompCog: RI: Medium: Understanding human planning through AI-assisted analysis of a massive chess dataset

计划，就是在心里模拟我们行动的后果的能力。这是人类智慧的一个重要方面——它让我们做出更有远见的选择，为别人对我们的决定可能做出的反应制定策略，并解决复杂的问题。然而，在实验室中研究计划一直具有挑战性，因为它需要从执行困难任务的高度积极的参与者那里获得大量数据。这个项目通过研究棋手如何在国际象棋中选择走法来解决这一挑战。国际象棋是一个独特的平台，可以探索个人如何形成复杂的多步骤计划，随着时间的推移获得专业知识，并通过与其他玩家的互动制定复杂的策略。在线国际象棋平台最近越来越受欢迎，导致数十亿局棋的详细记录可以使用计算方法进行分析。此外，能够以超人类水平下棋的人工智能（AI）系统为我们提供了一种新的工具，可以用来研究人类的规划和决策。通过将大量行为数据集与先进的人工智能相结合，该项目将促进人类认知新理论的发展和验证，以及新的人工智能系统的开发。更广泛的社会影响包括为需要规划的复杂任务开发更好的辅导系统的潜力，提高人类在这些任务中的表现，并提供关于人类规划局限性的见解，以及如何将其扩展到诸如教育，工业和公共福利等各个领域。该项目有三个不同的目标。首先，将对数据集进行分析，以探索有关人类规划的长期问题，例如对简化表示的依赖、搜索修剪和摊销。其次，本计划将检视个人纵向纪录的学习效果，以建立专业发展及其与社会互动的数学模型。最后，前两个目标的结果将用于创建一个能够预测国际象棋比赛中个人移动的认知模型。除了提供对人类认知的新见解外，该项目还将改进分析人类行为海量数据集的方法，为未来的研究创造一个可以利用的基础设施。此外，本项目中建模的认知过程将以计算术语实例化，以便将其纳入新的人工智能算法。最终，该项目旨在为认知科学、人工智能和国际象棋界做出贡献，从而在学术界、工业界和整个社会产生更广泛的影响。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。

Planning, the ability to mentally simulate the consequences of our actions. It is a crucial aspect of human intelligence – it lets us make choices that are more future-minded, strategize about how others might react to our decisions, and solve complex problems. However, studying planning in the laboratory has been challenging because it requires large amounts of data from highly motivated participants performing difficult tasks. This project addresses this challenge by studying how players select moves in the game of chess. Chess serves as a unique platform to explore how individuals form complex multi-step plans, acquire expertise over time, and develop sophisticated strategies through interaction with other players. Online chess platforms have recently gained popularity, leading to detailed records of billions of games that can be analyzed using computational methods. Furthermore, artificial intelligence (AI) systems capable of playing chess at superhuman levels provide a new tool we can use to study human planning and decision-making. By combining massive behavioral datasets and advanced AI, this project will facilitate the development and validation of new theories of human cognition, as well as the development of new AI systems. The broader social implications include the potential to develop better tutoring systems for complex tasks that require planning, improving human performance in these tasks, and providing insights about the limits of human planning and how they can be expanded that can be applied in various areas such as education, industry, and public welfare.The project has three distinct objectives. First, the datasets will be analyzed to explore long-standing questions about human planning, such as reliance on simplified representations, search pruning, and amortization. Second, the project will examine learning effects across individual longitudinal records to develop mathematical models of expertise development and its interplay with social interaction. Lastly, the findings from the first two objectives will be used to create a cognitive model capable of predicting individual moves in chess games. In addition to providing novel insights into human cognition, the project will improve methodologies for analyzing massive datasets of human behavior, creating an infrastructure that future studies can leverage. Additionally, the cognitive processes modeled in this project will be instantiated in computational terms, facilitating their incorporation into new AI algorithms. Ultimately, the project aims to contribute to cognitive science, AI, and the chess community, leading to broader impacts across academia, industry, and society at large.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.