Most recent research on multiagent reinforcement learning (MARL) has explored how to deploy cooperative policies for homogeneous agents. However, realistic multiagent environments may contain heterogeneous agents that have different attributes or tasks. The heterogeneity of the agents and the diversity of relationships cause the learning of policy excessively tough. To tackle this difficulty, we present a novel method that employs a heterogeneous graph attention network to model the relationships between heterogeneous agents. The proposed method can generate an integrated feature representation for each agent by hierarchically aggregating latent feature information of neighbor agents, with the importance of the agent level and the relationship level being entirely considered. The method is agnostic to specific MARL methods and can be flexibly integrated with diverse value decomposition methods. We conduct experiments in predator–prey and StarCraft Multiagent Challenge (SMAC) environments, and the empirical results demonstrate that the performance of our method is superior to existing methods in several heterogeneous scenarios.

Depression is an affective disorder that poses a serious threat to both mental and physical health. Utilizing fuzzy -based neural network models for the identification and screening of depression can facilitate early intervention and treatment. Intuitionistic fuzzy stochastic configuration networks (IFSCNs) utilize a cost-sensitive learning framework, which enhances generalization performance for solving binary classification problems. However, IFSCNs ignore the impact of the relative neighborhood density of imbalanced samples with outliers. To learn more discriminant information from class imbalance depression recognition task, in this paper, we propose a novel deep ensemble stochastic configuration network via graph intuitionistic fuzzy, termed as DeSCN-GIF. Specifically, we first use graph-based intuitionistic fuzzy method to determine the membership and non-membership functions through the relative neighborhood density of imbalanced samples; moreover, an incremental self-ensemble deep stochastic configuration framework is presented to learn multi-level discriminative features, in which graph intuitionistic fuzzy cost-sensitive least squares loss function and weighted supervision mechanisms are applied to determine the parameters of DeSCN-GIF. Experimental results on Chinese syllable-based imbalanced depression voice datasets show that DeSCN-GIF has better binary classification performance compared to other learning models such as IFSCN, DSCN, SCN, GE-IFRVFL-CIL, IFRVFL, EDRVFL, DRVFL, RVFL, and DIFL-TSVM.

In this article, the microscale removal characteristics of potassium dihydrogen phosphate (KDP) crystal were investigated based on a developed ultrasonic vibration-assisted scratch test apparatus. The common and ultrasonic scratch tests using a Berkovich indenter were conducted in the [210], [110], and [010] crystal directions of the (001) crystal plane. The results showed significant anisotropy in the material removal characteristics for the three directions on the (001) crystal plane of the KDP crystal. The smallest mean friction coefficient and the largest mean critical depth of the brittle–ductile transition were observed in the [110] crystal direction. For the ultrasonic scratch test, the load was relatively small, the removal process was smoother, the scratch debris was small and uniform, and the ductile removal rate was higher than for the common scratch test. The critical depths of the brittle–ductile transition during the common and ultrasonic scratching processes were determined for each crystal direction. Combined with the values of the mechanical properties of KDP crystal that were obtained from the indentation tests, a calculation method for the critical depths of the brittle–ductile transition of KDP crystal during the common and ultrasonic scratch tests was discussed. This method could be used to guide the optimal selection of cutting parameters in practical machining.

Stochastic configuration network (SCN) is an emerging type of random neural network that allocates node parameters via supervised mechanisms to ensure universal approximation capability. However, outliers and noise in realworld data can adversely affect SCN's classification performance. To enhance SCN for binary classification, we introduce intuitionistic fuzzy set concepts to propose intuitionistic fuzzy SCN (IFSCN). Unlike SCN, IFSCN allocates an intuitionistic fuzzy number to every sample by computing degrees of membership and non-membership, thereby crafting an optimal classifier through strategic weighting of samples to mitigate the influence of noise. Furthermore, stochastic configuration sparse autoencoders (SCSAE) effectively learn sparse features using L1 regularization. By integrating multiple SC-SAE models, we extract robust sparse feature representations. We then propose hierarchical IFSCN (IFHSCN) built on SC-SAE for enhanced accuracy. Comprehensive experiments on 8 benchmark datasets demonstrate IFSCN and IFHSCN achieve superior binary classification performance over state-of-the-art models like intuitionistic fuzzy twin SVM, kernel ridge regression, random vector functional link networks. Overall, this study successfully equips SCN for real-world noisy data via intuitionistic fuzzy sets and sparsity, providing an effective and scalable solution for robust classification.