Xu et al., 2022 - Google Patents
A Combat Decision Support Method Based on OODA and Dynamic Graph Reinforcement LearningXu et al., 2022
- Document ID
- 2743630288026446718
- Author
- Xu B
- Zeng W
- Publication year
- Publication venue
- 2022 34th Chinese Control and Decision Conference (CCDC)
External Links
Snippet
Modern network centric joint combat greatly increases the complexity of war, and the demand for intelligent decision-making is becoming stronger. In this paper, a combat decision support method based on graph neural network and reinforcement learning is …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Johnson | Artificial intelligence: a threat to strategic stability | |
CN110083971B (en) | Self-explosion unmanned aerial vehicle cluster combat force distribution method based on combat deduction | |
Duan et al. | Multiple UCAVs cooperative air combat simulation platform based on PSO, ACO, and game theory | |
Liu et al. | A mechanism for recognizing and suppressing the emergent behavior of UAV swarm | |
Fu et al. | Alpha C2–an intelligent air defense commander independent of human decision-making | |
CN109063819B (en) | Bayesian network-based task community identification method | |
Li et al. | Modified particle swarm optimization for BMDS interceptor resource planning | |
Qiu et al. | One-to-one air-combat maneuver strategy based on improved TD3 algorithm | |
CN115951709A (en) | Multi-unmanned aerial vehicle air combat strategy generation method based on TD3 | |
Li et al. | An Intelligent Algorithm for Solving Weapon-Target Assignment Problem: DDPG-DNPE Algorithm. | |
Xu et al. | A Combat Decision Support Method Based on OODA and Dynamic Graph Reinforcement Learning | |
Tang et al. | Close-in weapon system planning based on multi-living agent theory | |
Zhao et al. | Deep Reinforcement Learning‐Based Air Defense Decision‐Making Using Potential Games | |
Jia et al. | An operational effectiveness evaluation method of the swarming UAVs air combat system | |
Yu et al. | Method of Unknown Target Risk Analysis and Threat Assessment for UUVs | |
Zhang et al. | Defense success rate evaluation for UAV swarm defense system | |
Deng et al. | Research on intelligent decision technology for multi-UAVs prevention and control | |
Yuan | UAVS Task Assignment Based on Hybrid Swarm Intelligence Algorithm | |
Wang et al. | Research on Combat Effectiveness Based on Internet of Things | |
Liu et al. | Research on Decision–Making Method of Air Combat Embedded Training Based on Extended Influence Diagram | |
Guo et al. | Mission simulation and stealth effectiveness evaluation based on fighter engagement manager (FEM) | |
Shakirov | Russian thinking on AI integration and interaction with nuclear command and control, force structure, and decision-making | |
Liu et al. | Research on Individual Performance Index of Air Cluster Combat Aircraft Based on Differential Game Theory | |
Li et al. | Double Deep Q-learning for Anti-saturation Attack Problem of Warship Group | |
Fu et al. | Air defense intelligent weapon target assignment method based on deep reinforcement learning |