Search Results for author: Ziyan An

Found 5 papers, 1 papers with code

Formal Logic Enabled Personalized Federated Learning Through Property Inference

no code implementations15 Jan 2024 Ziyan An, Taylor T. Johnson, Meiyi Ma

Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT).

Formal Logic Personalized Federated Learning

EduSAT: A Pedagogical Tool for Theory and Applications of Boolean Satisfiability

1 code implementation15 Aug 2023 Yiqi Zhao, Ziyan An, Meiyi Ma, Taylor Johnson

Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) are widely used in automated verification, but there is a lack of interactive tools designed for educational purposes in this field.

Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications

no code implementations11 Jun 2023 Jiangwei Wang, Shuo Yang, Ziyan An, Songyang Han, Zhili Zhang, Rahul Mangharam, Meiyi Ma, Fei Miao

The STL requirements are designed to include both task specifications according to the objective of each agent and safety specifications, and the robustness values of the STL specifications are leveraged to generate rewards.

Multi-agent Reinforcement Learning reinforcement-learning

Fairguard: Harness Logic-based Fairness Rules in Smart Cities

no code implementations22 Feb 2023 Yiqi Zhao, Ziyan An, Xuqing Gao, Ayan Mukhopadhyay, Meiyi Ma

Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks.

Fairness

V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving

no code implementations17 Feb 2022 Yiming Li, Dekun Ma, Ziyan An, Zixun Wang, Yiqi Zhong, Siheng Chen, Chen Feng

Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving.

Autonomous Driving

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