SHF: Small: Explanation Logic

计算技术对人们生活的影响正在迅速增长。因此，对于计算系统来说，解释它们的行为并向用户保证为什么他们应该信任它们变得越来越重要。由于自动化任务的复杂性也在稳步增加，计算结果和决策的解释将是不平凡的，并可能包含大量的细节，这就带来了在正确的细节水平上提供正确信息的挑战。为了有效，解释必须动态地适应特定的用户和情况。本研究探讨了动态的、适应性强的解释的本质，以及它们如何有效地帮助用户理解和信任复杂计算系统的行为。该项目的新奇之处在于注重解释的动态性，以便于对用户做出灵活、自适应的反应，而该项目的影响则是未来计算系统更加透明，更广泛地被更多人接受，这个研究项目的总体目标是为支持解释的理解推理提供一个逻辑基础系统.基于对一种新的理解形态（不同于知识和信念）的定义，研究者正在发展一种动态的双主体解释逻辑（以用户和系统为主体），它允许描述理解的演化。该研究项目正在为解释逻辑生成一个动态模型，该模型将成为通用解释推理算法的基础，该算法将动态缩小的可能世界集与相应缩小的定制解释集进行映射。此外，该研究正在开发可定制的解释结构的概念，以支持根据特定用户需求定制的解释的有效沟通。该奖项反映了NSF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。

Computing technology has a rapidly growing impact on people's lives. Therefore, it becomes ever more important for computing systems to explain their behavior and to reassure users why they should trust them. Since the complexity of automated tasks is also steadily increasing, explanations of computation results and decisions will be non-trivial and potentially contain a large number of details, which poses the challenge of delivering the right information at the right level of detail. To be effective, explanations must dynamically adapt to specific users and situations. This research explores the nature of dynamic, adaptable explanations and how they can effectively help users to gain an understanding of and trust in the behavior of complex computing systems. The project’s novelty is the focus on the dynamic nature of explanations to facilitate flexible, adaptive responses to users, and the project's impacts are more transparent future computing systems, more widely accepted by more people, brought about by methods and tools that affect the design of future software.The overall goal of this research project is to develop a logical basis for reasoning about understanding in support of explanation systems. Based on the definition of a new modality of understanding (which is different from knowledge and belief), the investigator is developing a dynamic two-agent explanation logic (with user and system as agents), which allows the description of the evolution of understanding. The research project is producing a dynamic model for explanation logic that is to be the basis for a generic explanation inference algorithm that mirrors the dynamically shrinking set of possible worlds with a correspondingly shrinking set of tailored explanations. In addition, the research is developing concepts for tailorable explanation structures to support the effective communication of explanations that are customized to specific user needs.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.