CAREER: Artificial Intelligence Planning with Realistic Preference Models

这是一个为期四年的持续奖励的第一年。偏好模型决定在几个计划中选择哪一个。规划者使用与人类决策者相同的偏好模型是很重要的，因为规划者应该做出与人类用户相同的决策，否则规划者就没有多大用处。PI将研究如何构建比当前规划者更适合人类决策者偏好模型的规划者，通过将人工智能的建设性方法与效用理论的更具描述性的方法相结合，以利用这两个决策学科的优势并扩展人工智能规划者的适用性。PI将使用各种偏好模型研究最优、良好或接近最优（“令人满意”）的规划。他将探索如何利用复杂的顺序规划任务的结构来有效地解决由效用理论提出的现实偏好模型，重点是在高风险决策情况下的偏好模型。为此，他将重点研究利用AI现有规划者的表征变化，将具有非线性效用函数的规划任务转换为这些规划方法可以解决的其他规划任务，并研究使用满足规划方法时对原始规划任务造成的误差。这项研究将在管理环境危机情况的背景下进行，例如清理海洋溢油。

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.