AlphaZero has demonstrated the effectiveness of integrating reinforcement learning (RL) with search methods in perfect-information games such as chess and Go. However, extending this paradigm to imperfect-information games remains a significant challenge due to the presence of hidden information.Counterfactual Regret Minimization (CFR) provides theoretical convergence guarantees in such settings, recent advances like Recursive Belief-based Learning (ReBeL) have introduced a unified RL+Search framework by transforming imperfect-information games into perfect-information games over public belief states (PBS).Despite its theoretical appeal, ReBeL’s implementation based on multi-layer perceptron (MLP) is limited in its ability to capture the relational structure inherent in PBS. In this paper, we propose GNN-ReBeL, a novel approach that leverages Graph Neural Network (GNN) to explicitly model the strategic relationships among information states by representing PBS as graph-structured data. This graph-based representation enhances information propagation within the belief space while preserving convergence to a Nash equilibrium.Empirical results on benchmark imperfect-information games Liar’s Dice, show that GNN-ReBeL outperforms existing methods by achieving exploitability values that are only 33%-85% of those of the original MLP-based approach under comparable computational budgets. These findings underscore the advantages of structured belief representations for strategic reasoning in complex imperfect-information environments.

The integration of reinforcement learning with search algorithms has revolutionized AI performance in perfect-information games, with AlphaZero demonstrating superhuman abilities in chess, shogi, and Go. Counterfactual Regret Minimization (CFR) has been the traditional approach for imperfect-information games, but requires extensive precomputation of complete game strategies. Recursive Belief-based Learning (ReBeL) represents a breakthrough by combining self-play reinforcement learning with real-time search in imperfect-information settings, enabling superhuman performance in poker and other games with hidden information. A critical hyperparameter in ReBeL’s algorithm is the depth of subgames used during search. While deeper subgames potentially capture more strategic information, they also increase computational complexity. This paper presents a systematic investigation of the impact of subgame depth on ReBeL’s performance, convergence rate, and computational requirements. Through extensive experiments on benchmark imperfect-information games Liar’s Dice, we demonstrate that optimal subgame depth involves a non-trivial trade-off between solution quality and computational efficiency. Our findings provide practical guidance for implementing ReBeL in various game domains and contribute to the broader understanding of search depth parameterization in reinforcement learning algorithms for imperfect-information games.

ABSTRACT Wireless networks, as the foundation of the modern information society, rely crucially on network topology with the development of 6th generation mobile networks technologies. The network topology structure not only shapes the mechanism and functional dynamics of network evolution, but also reflects the communication relationship and information exchange among nodes. For this reason, wireless network topology inference has become a key research field in network science and the Internet of Things. Wireless network topology inference methods can be roughly divided into cooperative methods and non‐cooperative methods. The former needs to directly participate in the communication process of the target network to obtain detailed internal information, and its applicability is limited. In contrast, the latter infers the topology through external observation of data packet timing without the need to know the internal information of the network in advance, and has broader practicability. This paper first outlines the basic concepts and scope of topology inference, and briefly reviews the cooperative methods. Then, three types of non‐cooperative methods were comprehensively summarized: based on statistical learning, based on machine learning, and based on rule analysis. Using a unified dataset and evaluation metrics, the performance of four representative non‐cooperative topology inference algorithms is compared. Finally, this paper points out the challenges faced by network topology inference and proposes potential future research directions, aiming to provide theoretical support for the continuous development of this field.

With its remarkable capability for automated feature extraction, deep neural networks (DNNs) have achieved significant breakthroughs in numerous fields. However, recent studies indicate that deep models are vulnerable to adversarial attacks. In the automatic modulation recognition (AMR) task, the attacker injects imperceptible adversarial perturbations into the radio signals, causing the receiver to misidentify the adversarial examples as incorrect modulation patterns. In this article, we propose an input-independent universal adversarial perturbation (UAP) generation method to attack DNN-based AMR. The proposed method, termed Joint Metric-based Universal Adversarial Perturbation (JM-UAP), strives to enhance the aggressiveness of UAPs by minimizing the similarity metric between feature vectors extracted from benign examples and adversarial examples. To achieve this, feature vectors derived from the penultimate layer of the DNN are chosen as the optimization objects. The dissimilarity between these feature vectors is assessed by combining two metrics, the Adjusted cosine similarity and the Pearson correlation. Extensive experiments demonstrate that our approach significantly reduces the accuracy of AMR models, exhibits robust transferability across various deep models and multiple signal-to-noise ratios (SNRs) datasets, and performs well even in asynchronous scenarios.

Abstract Spectrum map is a database that stores multidimensional representations of spectrum situation information. It provides support for spectrum sensing and endows wireless communication networks with intelligence. However, the ubiquitous deployment of monitoring devices leads to huge costs of operation and maintenance. It indicates that an approach is needed to reduce the number of monitoring devices, but prevent the degradation of data granularity. Therefore, this paper focuses on the accurate construction of the spectrum map. It aims to infer the fine‐grained spectrum situation of the target region based on coarse‐grained observation. In order to solve this problem, an inference framework based on deep residual network is developed in this paper. In the case of rule deployment for sensing nodes, it adopts the idea of super resolution to improve the accuracy of the spectrum map. The framework is composed of two major parts: an inference network, which generates fine‐grained spectrum maps from coarse‐grained counterparts by using feature extraction module and upsampling construction module; and a fusion network, which considers the influence of environmental factors to further improve the performance. A large number of experiments on simulated datasets verify the effectiveness of the proposed method.

Lexicographical order dependencies (ODs) specify orders between list of attributes, and are proven useful in optimizing SQL queries with order by clauses. To find hidden ODs from dirty data in practice, in this paper we make a first effort to study the approximate OD discovery problem, aiming at automatically discovering ODs that hold on the data with some exceptions. (1) We adapt two error measures to ODs, prove their desirable properties, and present efficient algorithms for computing the measures and related lower and upper bounds. (2) We present an efficient approximate OD discovery algorithm that is well suited to the two error measures, with a set of pruning rules and optimization techniques. (3) We conduct extensive experiments to verify the effectiveness and scalability of our methods, using real-life and synthetic data.