Author name cluster

He Jiang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

2 author rows

ICRA Conference 2025 Conference Paper

Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding

He Jiang
Yutong Wang 0003
Rishi Veerapaneni
Tanishq Duhan
Guillaume Sartoretti
Jiaoyang Li 0001

Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently, this field has embraced learning-based methods, which reactively generate single-step actions based on individual local observations. However, it is still challenging for them to match the performance of the best search-based algorithms, especially in large-scale settings. This work proposes an imitation-learning-based LMAPF solver that introduces a novel communication module as well as systematic single-step collision resolution and global guidance techniques. Our proposed solver, Scalable Imitation Learning for LMAPF (SILLM), inherits the fast reasoning speed of learning-based methods and the high solution quality of search-based methods with the help of modern GPUs. Across six large-scale maps with up to $\mathbf{1 0, 0 0 0}$ agents and varying obstacle structures, SILLM surpasses the best learning- and search-based baselines, achieving average throughput improvements of $\mathbf{137. 7 \%}$ and $\mathbf{1 6. 0 \%}$, respectively. Furthermore, SILLM also beats the winning solution of the 2023 League of Robot Runners, an international LMAPF competition. Finally, we validated SILLM with 10 real robots and $\mathbf{1 0 0}$ virtual robots in a mock warehouse environment.

Details

AAAI Conference 2025 Conference Paper

Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding

Hongzhi Zang
Yulun Zhang
He Jiang
Zhe Chen
Daniel Harabor
Peter J. Stuckey
Jiaoyang Li

We study the problem of optimizing a guidance policy capable of dynamically guiding the agents for lifelong Multi-Agent Path Finding based on real-time traffic patterns. Multi-Agent Path Finding (MAPF) focuses on moving multiple agents from their starts to goals without collisions. Its lifelong variant, LMAPF, continuously assigns new goals to agents. In this work, we focus on improving the solution quality of PIBT, a state-of-the-art rule-based LMAPF algorithm, by optimizing a policy to generate adaptive guidance. We design two pipelines to incorporate guidance in PIBT in two different ways. We demonstrate the superiority of the optimized policy over both static guidance and human-designed policies. Additionally, we explore scenarios where task distribution changes over time, a challenging yet common situation in real-world applications that is rarely explored in the literature.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Speedup Techniques for Switchable Temporal Plan Graph Optimization

He Jiang
Muhan Lin
Jiaoyang Li

Multi-Agent Path Finding (MAPF) focuses on planning collision-free paths for multiple agents. However, during the execution of a MAPF plan, agents may encounter unexpected delays, which can lead to inefficiencies, deadlocks, or even collisions. To address these issues, the Switchable Temporal Plan Graph provides a framework for finding an acyclic Temporal Plan Graph with the minimum execution cost under delays, ensuring deadlock- and collision-free execution. Unfortunately, existing optimal algorithms, such as Mixed Integer Linear Programming and Graph-Based Switchable Edge Search (GSES), are often too slow for practical use. This paper introduces Improved GSES, which significantly accelerates GSES through four speedup techniques: stronger admissible heuristics, edge grouping, prioritized branching, and incremental implementation. Experiments conducted on four different map types with varying numbers of agents demonstrate that Improved GSES consistently achieves over twice the success rate of GSES and delivers up to a 30-fold speedup on instances where both methods successfully find solutions.

PDF Details DOI

EAAI Journal 2024 Journal Article

A novel crude oil price forecasting model using decomposition and deep learning networks

Yao Dong
He Jiang
Yunting Guo
Jianzhou Wang

Crude oil prices are susceptible to a variety of elements, including military, political, financial, and diplomatic forces, resulting in price fluctuations that are random, abrupt, and chaotic. Based on the chaotic nature of crude oil price series, this paper proposes a model for predicting crude oil prices that incorporates variational modal decomposition algorithm (VMD), phase space reconstruction technique (PSR), convolutional neural network (CNN), and bidirectional long and short-term memory network (BiLSTM). Specifically, the noises in the original data are removed using VMD. Next, the crude oil price is reconstructed using PSR. Then the reconstructed and denoised phase space is fed into the CNN-BiLSTM model for multi-step predictions. The empirical results show that the proposed model achieves the lowest MAPEs as 0. 662%, 2. 283%, 3. 829%, and 4. 44% and the lowest MSEs as 0. 335, 3. 942, 10. 047 and 13. 681. The Diebold Mariano (DM) test also demonstrates the proposed model’s superiority in the domain of crude oil price forecasting.

Details DOI

AAMAS Conference 2024 Conference Paper

Boosting Studies of Multi-Agent Reinforcement Learning on Google Research Football Environment: The Past, Present, and Future

Yan Song
He Jiang
Haifeng Zhang
Zheng Tian
Weinan Zhang
Jun Wang

Even though Google Research Football (GRF) was initially benchmarked and studied as a single-agent environment in its original paper [19], recent years have witnessed an increasing focus on its multi-agent nature by researchers utilizing it as a testbed for Multi-Agent Reinforcement Learning (MARL), especially in the cooperative scenarios. However, the absence of standardized environment settings and uni�ed evaluation metrics for multi-agent scenarios hampers the consistent understanding of various studies. Furthermore, the challenging 5 vs 5 and 11 vs 11 full-game scenarios have received limited thorough examination due to their substantial training complexities. To address these gaps, this paper extends the original environment by not only standardizing the environment settings and benchmarking cooperative learning algorithms across di�erent scenarios, including the most challenging full-game scenarios, but also by discussing approaches to enhance football AI from diverse perspectives and introducing related research tools for learning beyond multi-agent cooperation. Speci�cally, we provide a distributed and asynchronous population-based self-play framework with diverse pre-trained policies for faster training, two football-speci�c analytical tools for deeper investigation, and an online leaderboard for broader evaluation. The overall expectation of this work is to advance the study of Multi-Agent Reinforcement Learning both on and with Google Research Football environment, with the ultimate goal of deploying these technologies to real-world applications, such as sports analysis. ∗Equal Contribution †Corresponding Author This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024), N. Alechina, V. Dignum, M. Dastani, J. S. Sichman (eds.), May 6 – 10, 2024, Auckland, New Zealand. © 2024 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org).

PDF

IJCAI Conference 2024 Conference Paper

Guidance Graph Optimization for Lifelong Multi-Agent Path Finding

Yulun Zhang
He Jiang
Varun Bhatt
Stefanos Nikolaidis
Jiaoyang Li

We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that, while incorporating guidance, such as highways, can accelerate MAPF algorithms, this often results in a trade-off with solution quality. In addition, how to generate good guidance automatically remains largely unexplored, with current methods falling short of surpassing manually designed ones. In this work, we introduce the guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization as the task of optimizing its edge weights. We present two GGO algorithms to automatically generate guidance for arbitrary lifelong MAPF algorithms and maps. The first method directly optimizes edge weights, while the second method optimizes an update model capable of generating edge weights. Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in eight benchmark maps, and (2) our update model can generate guidance graphs for as large as 93 x 91 maps and as many as 3, 000 agents. We include the source code at: https: //github. com/lunjohnzhang/ggo_public. All optimized guidance graphs are available online at: https: //yulunzhang. net/publication/zhang2024ggo.

PDF Details DOI

IJCAI Conference 2024 Conference Paper

VF-Detector: Making Multi-Granularity Code Changes on Vulnerability Fix Detector Robust to Mislabeled Changes

Zhenkan Fu
Shikai Guo
Hui Li
Rong Chen
Xiaochen Li
He Jiang

As software development projects increasingly rely on open-source software, users face the risk of security vulnerabilities from third-party libraries. To address label and character noise in code changes, we present VF-Detector to automatically identifying bug-fix commits in actual noise development environment. VF-Detector consists of three componments: Data Pre-processing (DP), Vulnerability Confidence Computation (VCC) and Confidence Learning Denoising (CLD). The DP component is responsible for preprocessing code change data. The VCC component calculates code change confidence value for each bug-fix by extracting features at various granularity levels. The CLD component removes noise and enhances model robustness by pruning noisy data with confidence values and performing effort-aware adjustments. Experimental results demonstrate VF-Detector's superiority over state-of-the-art methods in EffortCost@L and Popt@L metrics on Java and Python datasets. The improvements were 6. 5% and 5% for Java, and 23. 4% and 17. 8% for Python.

PDF Details DOI

TIST Journal 2019 Journal Article

Combating Fake News

Karishma Sharma
Feng Qian
He Jiang
Natali Ruchansky
Ming Zhang
Yan Liu

The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users’ engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

Details DOI

MFCS Conference 2018 Conference Paper

Low Rank Approximation of Binary Matrices: Column Subset Selection and Generalizations

Chen Dan 0001
Kristoffer Arnsfelt Hansen
He Jiang
Liwei Wang 0001
Yuchen Zhou

Low rank approximation of matrices is an important tool in machine learning. Given a data matrix, low rank approximation helps to find factors, patterns, and provides concise representations for the data. Research on low rank approximation usually focuses on real matrices. However, in many applications data are binary (categorical) rather than continuous. This leads to the problem of low rank approximation of binary matrices. Here we are given a d x n binary matrix A and a small integer k < d. The goal is to find two binary matrices U and V of sizes d x k and k x n respectively, so that the Frobenius norm of A - U V is minimized. There are two models of this problem, depending on the definition of the dot product of binary vectors: The GF(2) model and the Boolean semiring model. Unlike low rank approximation of a real matrix which can be efficiently solved by Singular Value Decomposition, we show that approximation of a binary matrix is NP-hard, even for k=1. In this paper, our main concern is the problem of Column Subset Selection (CSS), in which the low rank matrix U must be formed by k columns of the data matrix, and we are interested in the approximation ratio achievable by CSS for binary matrices. For the GF(2) model, we show that CSS has approximation ratio bounded by k/2+1+k/(2(2^k-1)) and this is asymptotically tight. For the Boolean model, it turns out that CSS is no longer sufficient to obtain a bound. We then develop a Generalized CSS (GCSS) procedure in which the columns of U are generated from Boolean formulas operating bitwise on selected columns of the data matrix. We show that the approximation ratio achieved by GCSS is bounded by 2^(k-1)+1, and argue that an exponential dependency on k is seems inherent.

Details

NeurIPS Conference 2018 Conference Paper

Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements

Ankush Mandal
He Jiang
Anshumali Shrivastava
Vivek Sarkar

Identifying the top-K frequent items is one of the most common and important operations in large data processing systems. As a result, several solutions have been proposed to solve this problem approximately. In this paper, we identify that in modern distributed settings with both multi-node as well as multi-core parallelism, existing algorithms, although theoretically sound, are suboptimal from the performance perspective. In particular, for identifying top-K frequent items, Count-Min Sketch (CMS) has fantastic update time but lack the important property of reducibility which is needed for exploiting available massive data parallelism. On the other end, popular Frequent algorithm (FA) leads to reducible summaries but the update costs are significant. In this paper, we present Topkapi, a fast and parallel algorithm for finding top-K frequent items, which gives the best of both worlds, i. e. , it is reducible as well as efficient update time similar to CMS. Topkapi possesses strong theoretical guarantees and leads to significant performance gains due to increased parallelism, relative to past work.

PDF Details

IJCAI Conference 2017 Conference Paper

Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks

He Jiang
Yangqiu Song
Chenguang Wang
Ming Zhang
Yizhou Sun

Heterogeneous information networks (HINs) is a general representation of many real world applications. The difference between HIN and traditional homogeneous graphs is that the nodes and edges in HIN are with types. Then in the many applications, we need to consider the types to make the approach more semantically meaningful. For the applications that annotation is expensive, on natural way is to consider semi-supervised learning over HIN. In this paper, we present a semi-supervised learning algorithm constrained by the types of HINs. We first decompose the original HIN into several semantically meaningful sub-graphs based the meta-graphs composed of entity and relation types. Then we perform random walk over the sub-graphs to propagate the labels from labeled data to unlabeled data. After we obtain all the labels propagated by different trials of random walk guided by meta-graphs, we use an ensemble algorithm to vote for the final labeling results. We use two public available datasets, 20-newsgroups and RCV1 datasets to test our algorithm. Experimental results show that our algorithm is better than the traditional semi-supervised learning algorithms for HINs. One particular by-product of this work is that we show that previous random walk approach guided by meta-paths can be non-stationary, which is the major reason we propose a meta-graph guide random walk for semi-supervised learning over HINs.

PDF Details