CAREER: Artificial Intelligence Planning with Realistic Preference Models

职业：利用现实偏好模型进行人工智能规划

• 批准号: 0536375
• 负责人: Sven Koenig
• 金额: $ 6.71万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-15 至 2007-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0536375&HistoricalAwards=false
• 关键词: CAREER; Artificial; Intelligence; Planning; Realistic

This is the first year of funding of a 4-year continuing award. Preference models determine which one of several plans to prefer. It is important that planners use the same preference models as human decision makers because planners should make the same decisions as their human users, otherwise the planners are not of much use. The PI will investigate how to build planners that fit the preference models of human decision makers better than current planners, by combining constructive methods from artificial intelligence with more descriptive methods from utility theory in order to take advantage of the strengths of the two decision-making disciplines and to extend the applicability of Al planners. The PI will study optimal vs. good or near-optimal ("satisficing") planning with a variety of preference models. He win explore how to exploit the structure of complex sequential planning tasks to solve them efficiently for realistic preference models suggested by utility theory, with an emphasis on preference models in high-stakes decision situations. To this end, he will focus on representation changes that make use of existing planners from AI by transforming planning tasks with nonlinear utility functions into others that these planning methods can solve, and will study the errors that result for the original planning task when satisficing planning methods are used instead. The research will be performed in the context of managing environmental crisis situations, such as cleaning-up marine oil-spills.

这是为期4年的持续奖的第一年资助。偏好模型决定了几种方案中的哪一种。重要的是，规划者使用与人类决策者相同的偏好模型，因为规划者应该做出与人类用户相同的决定，否则规划者没有太大用处。PI将研究如何通过将人工智能的构造性方法与效用理论的更具描述性的方法相结合，建立比当前规划者更适合人类决策者偏好模型的规划者，以利用这两个决策学科的优势，并扩大AI规划者的适用性。PI将使用各种偏好模型研究最佳与良好或接近最佳(“满意”)的计划。他将探索如何利用复杂的顺序计划任务的结构来有效地解决效用理论提出的现实偏好模型，重点是高风险决策情况下的偏好模型。为此，他将专注于利用人工智能中现有规划者的表示变化，将具有非线性效用函数的计划任务转换为这些计划方法可以解决的其他任务，并将研究当使用令人满意的计划方法时，对原始计划任务造成的错误。这项研究将在管理环境危机情况的背景下进行，例如清理海洋石油泄漏。