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

RI：小：代理对非确定性和动态域的期望的研究

• 批准号: 1909879
• 负责人: Lifang He
• 金额: $ 35.04万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909879&HistoricalAwards=false
• 关键词: RI; Small; Study; Agent; Expectations

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.

社会越来越依赖(1)完全自主的系统，如自动驾驶汽车和自动收银机，以及虚拟体现的系统，如自动股票交易员和虚拟环境中的角色；以及(2)物理体现的平台中的半自动系统，如车辆中的Break系统和虚拟环境，如出行顾问。这个项目解决了以下问题：当这些代理执行操作过程时，如何计算代理操作的期望？这是一个基本的问题，因为预期可以用来检测代理人的行为何时异常；也就是说，当他们的行动方针偏离预期时。它们还可以用来解释代理的行为，方法是指示从代理自己的执行中期望得到什么。该项目还包括一项针对高中生的具体教育活动，与研究计划的特殊性紧密结合。该项目将为一个教育单位制定一个计划，提供以实践练习为中心的体验，通过评估自动代理是否正在执行其分配的任务来体验人工智能。通过这个教育单元，学生们将了解在评估自动人工智能系统是否按预期运行时面临的挑战。这个项目研究的是计算代理生成的行动过程预期的问题。具体地说，本项目将研究这一问题：(1)当行动方针是一项政策，说明该领域行动结果的不确定性时；(2)当该行动方针是一项计划，并且该领域包括以误差幅度为界的数值流畅时；(3)当该行动方针是一项政策，并且该领域包括以误差幅度为界限的数值流畅时。该项目对于用于产生行动方针的特定规划范式以及在发现差异时用于纠正行动方针的范式是不可知的。这项研究基于行动过程的两种表征：(1)数值流量值在误差范围内的顺序计划；(2)非确定性域的政策。这两个陈述都说明了行动执行中的不确定性。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks

• DOI: 10.1145/3447548.3467252
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Hang Yin;John Lee;Thomas Hartvigsen;Sihong Xie

Optimal Budget Allocation for Crowdsourcing Labels for Graphs

• 发表时间: 2023
• 作者: Adithya Kulkarni;Mohna Chakraborty;Sihong Xie;Qi Li

Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection

• DOI: 10.1145/3459637.3482325
• 发表时间: 2021-10
• 期刊: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
• 作者: Kai Burkholder;Kenny Kwock;Yuesheng Xu;Jiaxin Liu;Chao Chen;Sihong Xie

Monitoring Goal Driven Autonomy Agent’s Expectations Generated from Durative Effects

监控目标驱动的自主代理从持续效应中产生的期望

• DOI: 10.1007/978-3-030-82193-7_32
• 发表时间: 2021
• 期刊: Proceedings of SAI Intelligent Systems Conference IntelliSys 2021: Intelligent Systems and Applications
• 作者: Reifsnyder, Noah;Munoz-Avila, Hector
• 通讯作者: Munoz-Avila, Hector

Computing Agent’s Expectations in Nondeterministic Domains

计算代理在非确定性域中的期望

• 发表时间: 2021
• 期刊: PAIR
• 作者: Reifsnyder, Noah;Munoz-Avila, Hector
• 通讯作者: Munoz-Avila, Hector