Improving human reasoning with causal Bayes networks: a multimodal approach

该项目旨在通过采用以用户为中心、多模态、交互的方法来提高人类对复杂系统的因果和概率推理。该项目将探索新的综合视觉和口头方式来解释因果概率模型及其推理，以减少已知的人类推理困难，并研究如何通过优先考虑最有用的用户和上下文特定信息来减少认知负荷。预期的结果包括新颖的人工智能方法，使用户能够驱动推理过程，并加强对系统推理的信任。将在医疗和法律领域评估绩效，通过更好、更透明的推理和决策为最终用户带来巨大的潜在利益。

This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-specific information. Expected outcomes include novel AI methods that empower users to drive the reasoning process and strengthen trust in the system’s reasoning. Performance will be assessed in medical and legal domains, with significant potential benefits to end users from better, more transparent reasoning and decision making.