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.

社会正在越来越依靠（1）完全自主的系统，例如自动驾驶汽车和自动收银员以及虚拟股票交易者和虚拟环境中的角色； （2）在物理体现的平台中的半自主系统，例如车辆和虚拟环境中的断路系统，例如Trip Advisors。该项目解决了以下问题：当这些代理执行行动方案时，如何计算代理行动的期望？这是一个基本问题，因为可以使用期望来检测何时何时异常。也就是说，当他们的行动方案偏离期望时。他们还可以通过指示代理商自己的执行情况来指示对代理商的行为来解释代理商的行为。该项目还包括针对高中生的具体教育活动，与研究计划的特殊性紧密融合。该项目将制定一个计划，以通过评估自动化代理是否执行其分配的任务，为围绕AI的动手练习提供一种经验，从而为教育部门制定计划。 通过该教育部门，学生将对评估自动AI系统的行为的挑战表示赞赏。本项目研究计算代理商产生的行动过程的期望的问题。具体而言，该项目将调查此问题（1）当行动过程是在域中行动结果中不确定性的政策时； （2）当动作过程是一个计划时，域名包括数字流利，这些数字流利受到误差的边界； （3）当动作过程是策略时，域名包括数字流利，这些数字流利是由错误余量界定的。 对于用于生成行动进程的特定计划范式以及在检测到差异时用于校正行动方案的范式的特定计划范式方面，该项目是不可知的。该研究基于作用过程的两种表示：（1）在错误边缘内的数字流利和（2）非确定性域的策略的顺序计划。这两种表示都证明了行动执行的不确定性。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影响的审查标准来评估值得支持的。

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

Inconsistent Matters: A Knowledge-Guided Dual-Consistency Network for Multi-Modal Rumor Detection

• DOI: 10.1109/tkde.2023.3275586
• 发表时间: 2023-06
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Mengzhu Sun;Xi Zhang;Jianqiang Ma;Yazheng Liu