As complex adaptive system involves human and social factors (e.g., changing demands, competition and collaboration among agents), accurately modeling the complex features of adaptive agents and AI society is crucial for the effective analysis and governance of complex adaptive systems. However, existing modeling methods struggle to accurately represent these complex features, there is a gap between existing technologies and complex features modeling. In this context, this paper proposes a hierarchical model based on the computational experiments method, which consists of four layers (i.e., L1, L2, L3 and L4) modeling the autonomous, evolutionary, interactive, and emergent features respectively from adaptive agent to AI society. Additionally, taking intelligent transportation system as an example, a computational experiments system is constructed to demonstrate the effectiveness of the proposed model. This model builds a bridge between complex feature modeling and various technologies, thereby offering theoretical support for further research in complex adaptive systems.

Aerial-terrestrial network (ATN) framework is currently the dominant method of the internet of unmanned agents (IUA) for integrating both aerial vehicles and terrestrial sensors. In ATN scenarios, existing work shows that route planning utilizing deep reinforcement learning (DRL) is important as it can conserve energy for aerial vehicles or diminish network latency. However, utilizing DRL approaches in a multi-agent ATN environment may lead to inefficiencies when sharing original interaction data. In addition, direct data exchange between agents can raise significant privacy concerns. To address these challenges, this work develops a novel federated reinforcement learning (FRL) approach called FRIGE, which takes advantage of ensemble learning for DRL-based methods. Specifically, this paper develops a prioritized gradient ensemble technique for local DRL agents, which constructs twin networks to obtain prioritized gradients without incurring additional interactive costs. After aggregating local models on the server, the latest iteration of the global model is used to update the networks of both agents and their twin networks for subsequent rounds of training. Extensive experiments are conducted on two typical ATN route planning tasks to validate FRIGE’s advancement and generalization capabilities. The numerical results demonstrate that FRIGE not only enhances data sharing efficiency among all agents but also maintains privacy while delivering superior solutions for ATN route planning tasks.

The interpretability of neural networks has garnered significant attention. In the domain of computer vision, gradient-based feature attribution techniques like RectGrad have been proposed to utilize saliency maps to demonstrate feature contributions to predictions. Despite advancements, RectGrad falls short in category discrimination, producing similar saliency maps across categories. This paper pinpoints the ineffectiveness of threshold-based strategies in RectGrad for distinguishing feature gradients and introduces Class activation map Guided BackPropagation (CGBP) to tackle the issue. CGBP leverages class activation maps during backpropagation to enhance gradient selection, achieving consistent improvements across four models (VGG16, VGG19, ResNet50, and ResNet101) on ImageNet’s validation set. Notably, on VGG16, CGBP improves SIC, AIC, and IS scores by 10.3%, 11.5%, and 4.5%, respectively, compared to RectGrad while maintaining competitive DS performance. Moreover, CGBP demonstrates greater sensitivity to model parameter changes than RectGrad, as confirmed by a sanity check. The proposed method has broad applicability in scenarios like model debugging, where it identifies causes of misclassification, and medical image diagnosis, where it enhances user trust by aligning visual explanations with clinical insights.

Patient similarity search is a fundamental and important task in artificial intelligence-assisted medicine service, which is beneficial to medical diagnosis, such as making accurate predictions for similar diseases and recommending personalized treatment plans. Existing patient similarity search methods retrieve medical events associated with patients from Electronic Health Record (EHR) data and map them to vectors. The similarity between patients is expressed by calculating the similarity or dissimilarity between the corresponding vectors of medical events, thereby completing the patient similarity measurement. However, the obtained vectors tend to be high dimensional and sparse, which makes it hard to calculate patient similarity accurately. In addition, most of existing methods cannot capture the time information in the EHR, which is not conducive to analyzing the influence of time factors on patient similarity search. To solve these problems, we propose a patient similarity search method based on a heterogeneous information network. On the one hand, the proposed method uses a heterogeneous information network to connect patients, diseases, and drugs, which solves the problem of vector representation of mixed information related to patients, diseases, and drugs. Meanwhile, our method measures the similarity between patients by calculating the similarity between nodes in the heterogeneous information network. In this way, the challenges caused by high-dimensional and sparse vectors can be addressed. On the other hand, the proposed method solves the problem of inaccurate patient similarity search caused by the lack of use of time information in the patient similarity measurement process by encoding time information into an annotated heterogeneous information network. Experiments show that our method is better than the compared baseline methods.

Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang. Findings of the Association for Computational Linguistics: ACL 2025. 2025.