Cyber attacks and disturbances greatly impair the performance of formation tasks in multiagent systems (MASs). To achieve robust formation control against these challenges, this article proposes a generative adversarial learning framework that is theoretically transparent and practically applicable. Rather than relying on an end-to-end deep neural networks (DNNs) architecture, our work leverage a double robust structure that combine the representation capabilities of DNNs with established, theoretically grounded linear control theory, ultimately achieving a practical, learning-based robust formation for MASs. Initially, generative adversarial networks (GANs) are used to linearize agent dynamics under false data injection (FDI) attacks and external disturbances. Subsequently, a proportional-integral (PI) protocol is employed to achieve overall robust formation. We present rigorous theoretical analyses of both stages, demonstrating the guaranteed convergence of GANs training and the closed-loop formation errors. Our approach is directly validated through a series of physical experiments involving multi-quadrotors, demonstrating robustness against attacks and disturbances during formation flights, without the sim-to-real gap commonly encountered in learning-based control frameworks.

We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information. Besides discovering information flow patterns among users, we construct communities among users and develop the propagation forecasting capability, enabling tracing and understanding of how information is disseminated at a higher level. A demo video and more are available at https://info-pathways.github.io.

Hyundong Cho, Shuai Liu, Taiwei Shi, Darpan Jain, Basem Rizk, Yuyang Huang, Zixun Lu, Nuan Wen, Jonathan Gratch, Emilio Ferrara, Jonathan May. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

As social networks become further entrenched in modern society, it becomes increasingly important to understand and predict how information (e.g., news coverage of a given event) is propagated across social media (i.e., information pathway), which helps the understandings of the impact of real-world information. Thus, in this paper, we propose a novel task, Information Pathway Prediction (IPP), which depicts the propagation paths of a given passage as a community tree (rooted at the information source) on constructed community interaction graphs where we first aggregate individual users into communities formed around news sources and influential users, and then elucidate the patterns of information dissemination across media based on such community nodes. We argue that this is an important and useful task because, on one hand, community-level interactions offer more stability than those at the user level; on the other hand, individual users are often influenced by their community, and modeling community-level information propagation will help the traditional link-prediction problem. To tackle the IPP task, we introduce Lightning, a novel content-aware link prediction GNN model and demonstrate using a large Twitter dataset consisting of all COVID related tweets that Lightning outperforms state-of-the-art link prediction baselines by a significant margin.

Automatic evaluation metrics are essential for the rapid development of open-domain dialogue systems as they facilitate hyperparameter tuning and comparison between models.Although recently proposed trainable conversation-level metrics have shown encouraging results, the quality of the metrics is strongly dependent on the quality of training data.Prior works mainly resort to heuristic textlevel manipulations (e.g.utterances shuffling) to bootstrap incoherent conversations (negative examples) from coherent dialogues (positive examples).Such approaches are insufficient to appropriately reflect the incoherence that occurs in interactions between advanced dialogue models and humans.To tackle this problem, we propose DEAM, a Dialogue coherence Evaluation metric that relies on Abstract Meaning Representation (AMR) to apply semanticlevel Manipulations for incoherent (negative) data generation.AMRs naturally facilitate the injection of various types of incoherence sources, such as coreference inconsistency, irrelevancy, contradictions, and decrease engagement, at the semantic level, thus resulting in more natural incoherent samples.Our experiments show that DEAM 1 achieves higher correlations with human judgments compared to baseline methods on several dialog datasets by significant margins.We also show that DEAM can distinguish between coherent and incoherent dialogues generated by baseline manipulations, whereas those baseline models cannot detect incoherent examples generated by DEAM.Our results demonstrate the potential of AMRbased semantic manipulations for natural negative example generation.