Search Results (120)

This study explores strategic deception and counter-deception in multi-agent reinforcement learning environments for a police officer–robber game. The research is motivated by real-world scenarios where agents must operate with partial observability and adversarial intent. We develop a suite of progressively complex grid-based environments featuring dynamic goals, fake targets, and navigational obstacles. Agents are trained using deep Q-networks (DQNs) with game-theoretic reward shaping to encourage deceptive behavior in the robber and intent inference in the police officer. The robber learns to reach the true goal while misleading the police officer, and the police officer adapts to infer the robber’s intent and allocate resources effectively. The environments include fixed and dynamic layouts with varying numbers of goals and obstacles, allowing us to evaluate scalability and generalization. Experimental results demonstrate that the agents converge to equilibrium-like behaviors across all settings. The inclusion of obstacles increases complexity but also strengthens learned policies when guided by reward shaping. We conclude that integrating game theory with deep reinforcement learning enables the emergence of robust, deceptive strategies and effective counter-strategies, even in dynamic, high-dimensional environments. This work advances the design of intelligent agents capable of strategic reasoning under uncertainty and adversarial conditions. Full article

Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article

Objectives: We introduce the conceptual framework for the Adversarial Claim Robustness Diagnostics (ACRD) protocol, a novel tool for assessing how factual claims withstand ideological distortion. Methods: Based on semantics, adversarial collaboration, and the devil’s advocate approach, we develop a three-phase evaluation process combining baseline evaluations, adversarial speaker reframing, and dynamic AI calibration along with quantified robustness scoring. We introduce the Claim Robustness Index that constitutes our final validity scoring measure. Results: We model the evaluation of claims by ideologically opposed groups as a strategic game with a Bayesian-Nash equilibrium to infer the normative behavior of evaluators after the reframing phase. The ACRD addresses shortcomings in traditional fact-checking approaches and employs large language models to simulate counterfactual attributions while mitigating potential biases. Conclusions: The framework’s ability to identify boundary conditions of persuasive validity across polarized groups can be tested across important societal and political debates ranging from climate change issues to trade policy discourses. Full article

This paper addresses the optimization problem for mobile jamming assistance schemes in cognitive covert communication (CR-CC), where cognitive users adopt the underlying mode for spectrum access, while an unmanned aerial vehicle (UAV) transmits the same-frequency noise signals to interfere with eavesdroppers. Leveraging the inherent dynamic game-theoretic characteristics of covert communication (CC) systems, we propose a novel covert communication optimization algorithm based on generative adversarial networks (GAN-CCs) to achieve system-wide optimization under the constraint of maximum detection error probability. In GAN-CC, the generator simulates legitimate users to generate UAV interference assistance schemes, while the discriminator simulates the optimal signal detection of eavesdroppers. Through the alternating iterative optimization of these two components, the dynamic game process in CC is simulated, ultimately achieving the Nash equilibrium. The numerical results show that, compared with the commonly used multi-objective optimization algorithm or nonlinear programming algorithm at present, this algorithm exhibits faster and more stable convergence, enabling the derivation of optimal mobile interference assistance schemes for cognitive CC systems. Full article

To address the challenges of real-time decision-making and resource optimization in multi-agent cooperative interception tasks within dynamic environments, this paper proposes a hierarchical framework for reinforcement learning-based interception algorithm (HFRL-IA). By constructing a hierarchical Markov decision process (MDP) model based on dynamic game equilibrium theory, the complex interception task is decomposed into two hierarchically optimized stages: dynamic task allocation and distributed path planning. At the high level, a sequence-to-sequence reinforcement learning approach is employed to achieve dynamic bipartite graph matching, leveraging a graph neural network encoder–decoder architecture to handle dynamically expanding threat targets. At the low level, an improved prioritized experience replay multi-agent deep deterministic policy gradient algorithm (PER-MADDPG) is designed, integrating curriculum learning and prioritized experience replay mechanisms to effectively enhance the interception success rate against complex maneuvering targets. Extensive simulations in diverse scenarios and comparisons with conventional task assignment strategies demonstrate the superiority of the proposed algorithm. Taking a typical scenario of 10 agents intercepting as an example, the HFRL-IA algorithm achieves a 22.51% increase in training rewards compared to the traditional end-to-end MADDPG algorithm, and the interception success rate is improved by 26.37%. This study provides a new methodological framework for distributed cooperative decision-making in dynamic adversarial environments, with significant application potential in areas such as maritime multi-agent security defense and marine environment monitoring. Full article

Federated learning enables privacy-preserving model training across distributed clients, yet real-world deployments face statistical, system, and behavioral heterogeneity, which degrades performance and increases vulnerability to adversarial clients. Existing incentive mechanisms often neglect participant credibility, leading to unfair rewards and reduced robustness. To address these issues, we propose a Trust-Aware Incentive Mechanism (TAIM), which evaluates client reliability through a multi-dimensional trust model incorporating participation frequency, gradient consistency, and contribution effectiveness. A trust-weighted reward allocation is formulated via a Stackelberg game, and a confidence-based soft filtering algorithm is introduced to mitigate the impact of unreliable updates. Experiments on FEMNIST, CIFAR-10, and Sent140 demonstrate that TAIM improves accuracy by up to 4.1%, reduces performance degradation under adaptive attacks by over 35%, and ensures fairer incentive distribution with a Gini coefficient below 0.3. TAIM offers a robust and equitable FL framework suitable for heterogeneous edge environments. Full article

We propose a new framework for compile-time ciphertext synthesis in fully homomorphic encryption (FHE) systems. Instead of invoking encryption algorithms at runtime, our method synthesizes ciphertexts from precomputed encrypted basis vectors using only homomorphic additions, scalar multiplications, and randomized encryptions of zero. This decouples ciphertext generation from encryption and enables efficient batch encoding through algebraic reuse. We formalize this technique as a randomized module morphism and prove that it satisfies IND-CPA security. Our proof uses a hybrid game framework that interpolates between encrypted vector instances and reduces the adversarial advantage to the indistinguishability advantage of the underlying FHE scheme. This reduction structure captures the security implications of ciphertext basis reuse and structured noise injection. The proposed synthesis primitive supports fast, encryption-free ingestion in outsourced database systems and other high-throughput FHE pipelines. It is compatible with standard FHE APIs and preserves layout semantics for downstream homomorphic operations. Full article

Image steganography is a research field that focuses on covert storage and transmission technologies. However, current image hiding methods based on invertible neural networks (INNs) have limitations in extracting image features. Additionally, after experiencing the complex noise environment in the actual transmission channel, the quality of the recovered secret image drops significantly. The robustness of image steganography remains to be enhanced. To address the above challenges, this paper proposes a Mamba-based Robust Invertible Network (MRIN). Firstly, in order to fully utilize the global features of the image and improve the image quality, we adopted an innovative affine module, VMamba. Additionally, to enhance the robustness against joint attacks, we introduced an innovative multimodal adversarial training strategy, integrating fidelity constraints, adversarial games, and noise resistance into a composite optimization framework. Finally, our method achieved a maximum PSNR value of 50.29 dB and an SSIM value of 0.996 on multiple datasets (DIV2K, COCO, ImageNet). The PSNR of the recovered image under resolution scaling (0.5×) was 31.6 dB, which was 11.3% higher than with other methods. These results show that our method outperforms other current state-of-the-art (SOTA) image steganography techniques in terms of robustness on different datasets. Full article

Diversification and business expansion have become key strategies for modern business development, prompting many large companies to move from singular service models to diversified service strategies, ultimately evolving into comprehensive service ecosystems. Therefore, an in-depth understanding of the evolutionary patterns of service ecosystems is crucial for formulating efficient and effective management strategies and helping enterprises to make informed decisions during the service innovation process. At present, research on the evolution of service ecosystems largely lacks sufficient theoretical underpinning and focuses on the supply–demand relationship relationship, which reduces the credibility of research conclusions and ignores the influence of multiple factors. In this paper, the Lotka–Volterra (LV) model is introduced to service ecosystems and the model as a ternary framework that captures competition–cooperation dynamics among atomic and composite services. In addition, an agent-based computational experiment is designed to integrate adversarial games for decision-making and genetic algorithms for service evolution. Furthermore, the results indicate that moderate competition $\alpha \approx 0.5$ among atomic services maximizes composite service innovation and excessive cooperation $\alpha \to 0$ stifles it. Full article

This study introduces a novel framework for intelligent unmanned BVR maneuver control within the context of adversarial games. The emphasis lies on three pivotal aspects: situational awareness, maneuver decision-making, and precise maneuver control. Within this paradigm, our unmanned aerial vehicles (UAVs) can assimilate crucial situational information through constructed situational vectors and execute sophisticated maneuvers, effectively addressing the intricacies of dynamic flight environments and various unpredictable scenarios within the game setting. To achieve granular maneuver control, this research introduces the Priority Heading Polling–Random Observation Weight (PHP-ROW) method, underpinned by deep reinforcement learning. This approach integrates two primary components: (1) the priority heading polling (PHP) mechanism, which governs the extent of flight trajectories while emphasizing heading control, and (2) the random observation weight (ROW) technique, which adeptly moderates the influence of roll angle rewards during the learning phase. The superiority of the PHP-ROW method is showcased by contrasting it against the conventional proximal policy optimization (PPO) algorithm. Conclusively, the utility and efficacy of the presented framework are corroborated through human–machine adversarial game simulations in a hyper-realistic environment. This investigation provides foundational theoretical and empirical contributions to the realm of intelligent unmanned aerial maneuver control, promising significant implications for the evolution of aviation technology in adversarial game contexts. Full article

In the context of real-time strategy video games like StarCraft II, strategic decision-making is a complex challenge that requires adaptability and precision. This research creates a mixed recommendation system that uses causal models and deep neural networks to improve its ability to suggest the best strategies based on the resources and conditions of the game. PySC2 and the official StarCraft II API collected data from 100 controlled matches, standardizing conditions with the Terran race. We created fake data using a Conditional Tabular Generative Adversarial Network to address data scarcity situations. These data were checked for accuracy using Kolmogorov–Smirnov tests and correlation analysis. The causal model, implemented with PyMC, captured key causal relationships between variables such as resources, military units, and strategies. These predictions were integrated as additional features into a deep neural network trained with PyTorch. The results show that the hybrid system is 1.1% more accurate and has a higher F1 score than a pure neural network. It also changes its suggestions based on the resources it has access to. However, certain limitations were identified, such as a bias toward offensive strategies in the original data. This approach highlights the potential of combining causal knowledge with machine learning for recommendation systems in dynamic environments. Full article

The construction industry is inherently marked by high uncertainty levels driven by its complex processes. These relate to the bidding environment, resource availability, and complex project requirements. Accurate bid pricing under such uncertainty remains a critical challenge for contractors seeking a competitive advantage while managing risk exposure. This exploratory study integrates artificial intelligence (AI) into game theory models in an AI-assisted framework for bid pricing in construction. The proposed model addresses uncertainties from external market factors and adversarial behaviours in competitive bidding scenarios by leveraging AI’s predictive capabilities and game theory’s strategic decision-making principles; integrating extreme gradient boosting (XGBOOST) + hyperparameter tuning and Random Forest classifiers. The key findings show an increase of 5–10% in high-inflation periods with a high model accuracy of 87% and precision of 88.4%. AI can classify conservative (70%) and aggressive (30%) bidders through analysis, demonstrating the potential of this integrated approach to improve bid accuracy (cost estimates are generally within 10% of actual bid prices), optimise risk-sharing strategies, and enhance decision making in dynamic and competitive environments. The research extends the current body of knowledge with its potential to reshape bid-pricing strategies in construction in an integrated AI–game-theoretic model under uncertainty. Full article

In modern naval confrontation systems, adversarial underwater unmanned vehicles (UUVs) pose significant challenges, which are deployed on unmanned aerial vehicles (UAVs) due to their inherent mobility and positional uncertainty. Effective neutralization threats demand sophisticated coordination strategies between distributed agents under partial observability. This paper proposes a novel Knowledge-Enhanced Multi-Agent Deep Reinforcement Learning (MADRL) framework for coordinating UAV swarms against adversarial UUVs in asymmetric confrontation scenarios, specifically addressing three operational modes: area surveillance, summoned interception, and coordinated countermeasures. Our framework introduces three key innovations: (1) a probabilistic adversarial model integrating prior intelligence and real-time UAV sensor data to predict underwater trajectories; (2) a Multi-Agent Double Soft Actor–Critic (MADSAC) algorithm, addressing Red team coordination challenges. Experimental validation demonstrates superior performance over baseline methods in Blue target detection efficiency (38.7% improvement) and successful neutralization rate (52.1% increase), validated across escalating confrontation scenarios. Full article

This advanced exploration of integrating cross-modal Artificial-Intelligence-Generated Content (AIGC) within the Unity3D game engine seeks to elevate the diversity and coherence of image generation in game art creation. The theoretical framework proposed dives into the seamless incorporation of generated visuals within Unity3D, introducing a novel Generative Adversarial Network (GAN) structure. In this architecture, both the Generator and Discriminator embrace a Transformer model, adeptly managing sequential data and long-range dependencies. Furthermore, the introduction of a cross-modal attention module enables the dynamic calculation of attention weights between text descriptors and generated imagery, allowing for real-time modulation of modal inputs, ultimately refining the quality and variety of generated visuals. The experimental results show outstanding performance on technical benchmarks, with an inception score reaching 8.95 and a Frechet Inception Distance plummeting to 20.1, signifying exceptional diversity and image quality. Surveys reveal that users rated the model’s output highly, citing both its adherence to text prompts and its strong visual allure. Moreover, the model demonstrates impressive stylistic variety, producing imagery with intricate and varied aesthetics. Though training demands are extended, the payoff in quality and diversity holds substantial practical value. This method exhibits substantial transformative potential in Unity3D development, simultaneously improving development efficiency and optimizing the visual fidelity of game assets. Full article

The rotational speed and load torque of wind turbine gearboxes can vary widely during operation, which has an obvious impact on the gearbox fault diagnosis carried out based on vibration signals. To address this problem, this paper proposes a fault diagnosis method that introduces an adversarial training mechanism and designs a game learning strategy among the feature extractor, fault recognizer, rotational speed estimator, and load estimator. In this way, the network tends to acquire fault features with weaker correlation with rotational speed and load and thus improves the performance of the fault diagnosis network in the face of the samples from the rotational speed and load ranges that are not covered by the training set. At the same time, in order to verify the effectiveness of the proposed method, in this paper, we have designed an experimental platform for wind turbine gearbox scaling, carried out simulation experiments of variable speed and torque faults, collected experimental data, and constructed a variable speed and load fault dataset. Comparing the proposed method with the baseline model, when confronted with data from RPMs or load ranges not covered by the training set, the accuracy of the baseline model drops by anywhere from 10.54% to 16.46%, while the accuracy of the method drops by only 1.39%. The results show that the method can effectively improve the performance of the fault diagnosis network when facing a variation of speed and load. Full article