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

计划，在心理上模拟我们行为的后果的能力。它是人类智力的一个关键方面--它让我们做出更具前瞻性的选择，对其他人可能对我们的决定做出的反应制定战略，并解决复杂的问题。然而，在实验室中研究规划一直是具有挑战性的，因为它需要来自执行困难任务的高积极性参与者的大量数据。这个项目通过研究棋手如何在国际象棋比赛中选择棋步来解决这一挑战。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来制定复杂的策略。在线国际象棋平台最近变得流行起来，导致了数十亿局游戏的详细记录，可以使用计算方法进行分析。此外，能够在超人水平上下国际象棋的人工智能(AI)系统为我们提供了一种新的工具，我们可以用来研究人类的规划和决策。通过将海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开发和验证，以及新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可能性，提高人类在这些任务中的表现，以及提供关于人类规划的局限性以及如何将其扩展到教育、工业和公共福利等不同领域的见解。该项目有三个不同的目标。首先，将对数据集进行分析，以探索关于人类规划的长期存在的问题，例如依赖简化表示法、搜索修剪和摊销。其次，该项目将检查个人纵向记录的学习效果，以开发专业知识发展及其与社会互动的相互作用的数学模型。最后，来自前两个目标的发现将被用来创建一个认知模型，该模型能够预测国际象棋游戏中的个人走法。除了为人类认知提供新的见解外，该项目还将改进分析人类行为的海量数据集的方法，创建一个未来研究可以利用的基础设施。此外，该项目中建模的认知过程将在计算方面实例化，便于将其整合到新的人工智能算法中。最终，该项目旨在为认知科学、人工智能和国际象棋社区做出贡献，在学术界、行业和社会产生更广泛的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.