Systems, Algorithms, and Cognitive Models to Predict Individual Human Reasoning (PREDIR)

该项目专注于认知人工智能研究的核心挑战：开发一个能够预测个人信息处理的系统，特别是基于少数观察到的例子的未来结论。通过关注个体，该项目与认知心理学等方法不同，认知心理学由于统计和数据分析的原因，忽略了个体实验参与者的异常行为，因此只关注聚合实验数据的认知模型和一般认知现象的识别。在这个项目中，我们重新分析数据，专注于个人的角度，开发能够预测个人的未来结论的计算系统，给出稀疏的背景信息，并分析和评估现有的方法在认知心理学，认知科学和数据驱动的方法，如推荐系统的性能。特别是，我们将确定，然后开发和系统地改进方法，使准确的预测，但也确定数据驱动的上限。此外，人类推理的机制以及这些见解如何反映在自适应认知建模方法的自动开发中的问题将得到澄清。该项目开发认知AI模型，能够自适应和预测性地调整个人思维过程并预测结论。

This project focuses on a central challenge of cognitive AI research: to develop a system that is able to predict an individual's information processing and in particular future conclusions of based on few observed examples. By focusing on the individual, this project differs from approaches in, for example, cognitive psychology, which, due to statistical, data-analytical reasons, disregards deviant behavior of individual experimental participants and thus focuses only on cognitive models for aggregated experimental data and identification of general cognitive phenomena. In this project, we reanalyze data with a focus on an individual perspective, develop computational systems capable of predicting an individual's future conclusions given sparse background information, and analyze and evaluate the performance of existing approaches in cognitive psychology, cognitive science, and data-driven methods such as recommender systems. In particular, we will identify and then develop and systematically improve methods that enable accurate predictions but also identify data-driven upper bounds. Furthermore, the question of which mechanisms underlie human reasoning and how these insights can be reflected in an automatic development of adaptive cognitive modeling approaches will be clarified. This project develops cognitive AI models that are able to adaptively and predictively adjust to individual thought processes and predict conclusions.