Deep Learning Algorithms and Game Theory Models for Intelligent Information Processing and Decision-Making

Dear Colleagues,​​

Over the past decade, deep learning algorithms have revolutionized intelligent information processing, dominating advancements in speech recognition, computer vision, and natural language processing. While these models excel in data-rich tasks (e.g., image recognition, translation), extending AI capabilities to data-scarce, multi-agent, and dynamically adversarial environments ​remains a critical frontier. Game theory models—grounded in strategic decision-making—provide the mathematical rigor to navigate these complexities, enabling AI systems to optimize interactions in competitive or cooperative settings. This Special Issue aims to showcase cutting-edge advancements in computer vision, deep learning, and AI, with a focus on integrating game theory for enhanced decision-making. Topics of interest include, but are not limited to, the following:

1. Deep Learning with Limited Data​​

• Leveraging transfer learning, meta-learning, and synthetic data generation (e.g., GANs) to overcome data scarcity, enhanced by game-theoretic reward shaping for strategic exploration. ​

2. Unsupervised Deep Learning Technology

• Autoencoders, clustering algorithms (e.g., deep embedded clustering), and generative models (e.g., VAEs) for extracting patterns from unlabeled data in anomaly detection, dimensionality reduction, and feature learning.

3. Game Theory Models for Strategic AI​​

• Nash equilibria, Stackelberg competitions, and mechanism design for modeling multi-agent interactions in negotiation, resource allocation, and adversarial scenarios.

4. Knowledge Graphs for Decision Explainability​​

• Structuring domain knowledge (e.g., game rules, tactical patterns) into graph-based representations to enable interpretable reasoning and real-time strategy optimization in incomplete-information games.

5. Neural Rendering and Simulation​​

• Combining neural networks with physics-based rendering for synthetic environments, where game theory refines agent behavior in simulated strategic interactions.

6. Deep Reinforcement Learning with Game-Theoretic Incentives​​

• Actor–critic architectures and multi-agent reinforcement learning (MARL) guided by equilibria concepts (e.g., Pareto optimality) to balance cooperation and competition.

7. Language–Vision–Action Integration​​

• Multimodal transformers (e.g., CLIP) combined with game-theoretic negotiation for human–AI collaboration in robotics or virtual assistants.

8. Industrial AI with Strategic Optimization​​

• Game theory-driven solutions for predictive maintenance, supply chain coordination, and market design, supported by knowledge graphs for constraint modeling.

9. Deep Learning for Medical Image Analysis​

• Applying transfer learning and self-supervised methods to improve disease diagnosis, segmentation, and classification in medical imaging with limited annotated data.

10. Deep Learning for Efficient Detection and Segmentation

• Developing lightweight architectures (e.g., MobileNet) and attention mechanisms optimize real-time object detection and semantic segmentation in resource-constrained environments.

11. LLM-Driven Multi-Agent Systems​

• LLM-driven multi-agent systems (MAS)​​ leverage multiple specialized large language model (LLM) agents that collaborate to solve complex tasks beyond the capabilities of single-agent systems.

12. Ethical AI via Mechanism Design​​

• Designing incentive-compatible systems that align multi-agent objectives with fairness, accountability, and regulatory compliance.

Dr. Wenjun Ma
Dr. Xiaomao Fan
Dr. Chen Li
Guest Editors

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• deep learning
• game theory model
• multi-agent systems 
• decision-making
• neural rendering
• ethical AI