Collaborative Research: RI: III: SHF: Small: Multi-Stakeholder Decision Making: Qualitative Preference Languages, Interactive Reasoning, and Explanation

协作研究：RI：III：SHF：小型：多利益相关者决策：定性偏好语言、交互式推理和解释

• 批准号: 2225823
• 负责人: Samik Basu
• 金额: $ 29.92万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225823&HistoricalAwards=false
• 关键词: Collaborative; Research; RI; III; SHF

The ability to express and reason about preferences over a set of alternatives is central to rational decision-making in a broad range of applications, such as product design, public policy, health care, information security, and privacy, among others. Because of the lack of quantitative preferences in many practical settings, there is increasing interest in methods for representing and reasoning with qualitative preferences. Furthermore, practical decision making scenarios typically involve multiple stakeholders, with possibly conflicting preferences, and the preferences of some stakeholders may sometimes override those of others, e.g., because of the relative positions of the stakeholders within an organization. However, existing preference languages and methods are limited to the single stakeholder setting. Against this background, this project brings together a team of researchers with complementary expertise in formal methods, artificial intelligence, and preference reasoning to develop methods and tools for representing and reasoning with multi-stakeholder preferences. The practical open-source multi-stakeholder decision support tools resulting from the project will significantly lower the barrier to the applications of AI and formal methods for multi-stakeholder decision making in a number of domains. The project enhances research-based training of graduate and undergraduate students, including females and members of other under-represented groups, at ISU and PSU in artificial intelligence, formal methods, and related areas of national importance. Broad dissemination of research results (including publications, open source software, data, tutorials, course materials), incorporation of research results into undergraduate and graduate curricula in Computer Science, Information Sciences and Technology, Data Sciences, and related disciplines, and outreach to targeted application domains e.g., health, public policy, security and privacy, that would benefit from advanced tools for multi-stakeholder decision-making further enhance the broader impacts of the project.The primary intellectual merit of the project centers around substantial advances in the current state-of-the-art in languages, algorithms, and software for multi-stakeholder representation and reasoning with preferences. The researchers will develop Generalized Conditional Relative Importance and Preference Theory (GCRIPT), an expressive language for multi-stakeholder preference representation that subsumes existing preference languages. The resulting preference reasoners will be able to (a) analyze preferences expressed in GCRIPT, (b) reason with the preferences of multiple stakeholders, taking into account not only their individual preferences, but also hierarchies that give precedence to the preferences of some stakeholders over those of others, and (c) offer easy-to-understand explanations of the preferred choices as well as their impacts on the stakeholders. The project will also enhance the underlying model checking techniques that form the core technology for the preference reasoning framework; e.g., in the areas of incremental model checking, counter-example analysis and justification. The resulting advances in knowledge representation and formal methods contribute to AI systems that substantially augment and extend human capabilities in multi-stakeholder decision making.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.

表达和推理对一组替代方案的偏好的能力对于广泛应用中的理性决策至关重要，例如产品设计、公共政策、医疗保健、信息安全和隐私等。由于在许多实际环境中缺乏定量偏好，人们对用定性偏好进行表示和推理的方法越来越感兴趣。此外，实际决策场景通常涉及多个利益相关者，可能存在相互冲突的偏好，并且某些利益相关者的偏好有时可能会凌驾于其他利益相关者的偏好之上，例如，由于组织内利益相关者的相对位置。然而，现有的偏好语言和方法仅限于单一利益相关者设置。在此背景下，该项目汇集了在形式方法、人工智能和偏好推理方面具有互补专业知识的研究人员团队，以开发用于表示和推理多利益相关者偏好的方法和工具。该项目产生的实用开源多利益相关者决策支持工具将显着降低人工智能和多利益相关者决策正式方法在多个领域的应用障碍。该项目加强了 ISU 和 PSU 研究生和本科生（包括女性和其他代表性不足群体的成员）在人工智能、形式化方法和国家重要相关领域的基于研究的培训。广泛传播研究成果（包括出版物、开源软件、数据、教程、课程材料），将研究成果纳入计算机科学、信息科学与技术、数据科学及相关学科的本科生和研究生课程，并推广到目标群体应用领域，例如健康、公共政策、安全和隐私，将受益于多利益相关者决策的先进工具，进一步增强该项目的更广泛影响。该项目的主要智力价值集中在当前的重大进展上最先进的语言、算法和软件，用于多利益相关者表示和偏好推理。研究人员将开发广义条件相对重要性和偏好理论（GCRIPT），这是一种包含现有偏好语言的多利益相关者偏好表示的表达语言。由此产生的偏好推理器将能够 (a) 分析 GCRIPT 中表达的偏好，(b) 对多个利益相关者的偏好进行推理，不仅考虑他们的个人偏好，还考虑优先考虑某些利益相关者偏好的层次结构其他人的选择，以及 (c) 对首选选择及其对利益相关者的影响提供易于理解的解释。该项目还将增强底层模型检查技术，这些技术构成偏好推理框架的核心技术；例如，在增量模型检查、反例分析和论证等领域。由此产生的知识表示和形式化方法的进步有助于人工智能系统大大增强和扩展人类在多利益相关者决策中的能力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响进行评估，被认为值得支持审查标准。