RI: Small: A Study of Agent's Expectations for Nondeterministic and Dynamic Domains

社会越来越依赖于(1)完全自主的物理系统，如自动驾驶汽车和自动收银员，以及虚拟系统，如自动股票交易员和虚拟环境中的角色；(2)物理平台中的半自主系统，如车辆中的制动系统和虚拟环境，如旅行顾问。这个项目解决了以下问题：当这些代理执行一个行动过程时，如何计算代理行动的期望？这是一个基本问题，因为期望可以用来检测代理的行为何时异常；也就是说，当他们的行动偏离预期时。它们也可以用来解释代理的行为，通过指示从代理自己的执行中期望得到什么。该项目还包括针对高中生的具体教育活动，该活动与研究项目的特殊性紧密结合。该项目将为教育单位制定一项计划，通过评估自动代理是否执行其分配的任务，提供以人工智能实践练习为中心的体验。通过这个教育单元，学生们将了解到评估自动化人工智能系统是否按预期运行所面临的挑战。这个项目研究的问题是计算由一个代理产生的行动过程的期望。具体来说，本项目将研究以下问题：(1)当行动过程是一个策略时，该策略考虑了领域中行动结果的不确定性；(2)当行动过程是一个计划，并且领域包括有误差范围的数字流畅时；(3)当行动过程是一项政策，并且领域包括有误差范围的数字流利时。对于用于产生行动方针的特定规划范例，以及在发现差异时用于纠正行动方针的范例，该项目是不可知的。该研究基于行动过程的两种表示：(1)在误差范围内数值流畅的顺序计划和(2)非确定性领域的策略。这两种表示都说明了行动执行中的不确定性。该奖项反映了美国国家科学基金会的法定使命，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。

Society is increasing relying on both (1) fully autonomous systems physically-embodied such as self-driving cars and automated cashiers as well as virtually-embodied such as automated stock traders and characters in virtual environments; and (2) semi-autonomous systems in physically-embodied platforms such as the break system in vehicles and virtual environments such as trip advisors. This project addresses the following question: when these agents are executing a course of action, how to compute the expectations of the agent's actions? This is a fundamental question as expectations can be used to detect when the agent's behavior is anomalous; that is, when their course of action deviates from the expectations. They can also be used to explain the agent's behavior by indicating what to expect from the agent's own execution. This project also includes a concrete educational activity targeting high school students that is tightly integrated with the particularities of the research program. The project will develop a plan for an educational unit providing an experience centered around a hands-on exercise experiencing AI by evaluating if an automated agent is performing its assigned tasks. Through that educational unit, students will gain an appreciation of the challenges in assessing if automated AI systems are behaving as intended.This project studies the problem of computing the expectations of course of action generated by an agent. Specifically, this project will investigate this problem (1) when the course of action is a policy that accounts for non-determinism in the outcome of the actions in the domain; (2) when the course of action is a plan and the domain includes numeric fluents bounded by margins of error; (3) when the course of action is a policy and the domain includes numeric fluents bounded by margins of error. The project is agnostic with regard to the particular planning paradigm used to generate the course of action and with regard to the paradigm used for correction of the course of action when a discrepancy is detected. The study is based on two representations of the courses of action: (1) sequential plans with numeric fluents valued within margins of errors and (2) policies for non-deterministic domains. Both of these representations account for uncertainty in the execution of the actions.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.