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.

在广泛的应用程序中（例如产品设计，公共政策，医疗保健，信息安全和隐私等）等广泛的应用程序中，表达和理性的偏好能力和理由是理性决策的核心。由于在许多实际环境中缺乏定量偏好，因此对代表和推理的方法具有越来越多的兴趣。此外，实际决策方案通常涉及多个利益相关者，可能会有矛盾的偏好，而某些利益相关者的偏好有时可能会覆盖其他利益相关者，例如，由于组织在组织中的相对立场，因此有时可能会覆盖其他利益相关者。但是，现有的偏好语言和方法仅限于单个利益相关者设置。在这种背景下，该项目汇集了一群研究人员，具有正式方法，人工智能和偏好推理方面的补充专业知识，以开发以多利益相关者偏好来代表和推理的方法和工具。该项目产生的实用开源多方利益相关者决策支持工具将大大降低AI和正式方法在许多领域中的多方利益相关者决策的应用的障碍。该项目增强了基于研究的研究生和本科生的培训，包括女性以及其他代表性不足的群体，在人工智能，正式方法以及具有国家重要性的相关领域的ISU和PSU的其他培训。广泛传播研究结果（包括出版物，开源软件，数据，教程，课程材料），将研究结果纳入本科和研究生课程，计算机科学，信息科学和技术，数据科学和相关学科，以及对目标应用领域的范围内的范围，例如健康，公共政策，安全，安全和隐私权，将受益于范围，安全性，安全性，安全性和隐私权，并将其受益于业务范围。该项目的智力优点围绕着当前最新的语言，算法和软件，用于多利益相关者代表和推理，并具有偏好的推理。研究人员将开发一般的有条件相对重要性和偏好理论（Gcript），这是一种符合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者将能够（a）分析在gcript中表达的偏好，（b）具有多个利益相关者的偏好的原因，不仅要考虑到他们的个人偏好，而且还考虑了层次结构，这些层次结构为某些利益相关者而不是其他利益相关者的偏好提供了优先考虑，并且（c）可以轻松地塑造对他们偏好的选择，并及其偏好的选择及其对偏好的影响以及及其及其及其及其影响的影响。该项目还将增强构成偏好推理框架的核心技术的基本模型检查技术；例如，在增量模型检查的领域，反示例分析和理由。随之而来的知识表示和形式方法的进步有助于AI系统，这些系统实质上扩大了多方利益相关者决策的人类能力。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持的。