Llm-Driven Multi-Agent Social Network Simulation: Interdisciplinary Integration And Cutting-Edge Development,
2026
Academy of Military Science, Beijing 100097, China
Llm-Driven Multi-Agent Social Network Simulation: Interdisciplinary Integration And Cutting-Edge Development, Jiting Li, Yi Sun, Yirong Wang, Yiqin Lin, Jun Jia, Gangsong Ding
Journal of System Simulation
Abstract: The breakthrough of LLMs has provided powerful tools for social network research, advancing multi-agent social network simulation into a new era. This review systematically examined recent progress in LLM-driven multi-agent social network simulation research through a integrated perspective of multi-disciplines such as artificial intelligence, psychology, communication studies, and sociology. A three-tiered research system, which has gradually formed in this field and encompassed micro-level individual behaviors, meso-level interactive relations, and macro-level system emergence, was summarized. At the micro-level, research focuses on individual human behavior simulation, and numerous studies are dedicated to developing human-like agents with complex cognitive and affective architectures …
An Adaptive Robot Path Planning Based On Improved Rea* Algorithm,
2026
School of Mathematics and Statistics, Xidian University, Xi'an 710126, China
An Adaptive Robot Path Planning Based On Improved Rea* Algorithm, Ling Zhu, Jing Li, Zhaohui Zhang
Journal of System Simulation
Abstract: In order to improve the computational efficiency and path smoothness in a robot's global path planning, an adaptive robot path planning strategy based on an improved unilateral rectangle expansion A*(REA*) algorithm was proposed. The robot's operational safety was ensured by setting a buffer around obstacles. A passable interval formed by unilateral rectangle expansion was used as the operation unit, and bidirectional alternating search was combined to enhance the path planning efficiency. Inspired by potential field theory, the evaluation function was optimized by introducing a vector form to achieve fast adaptive obstacle avoidance. A new path planning strategy was proposed …
Evolutionary Game-Based Analysis Of Responses To Hallucinations In Generative Artificial Intelligence,
2026
School of Economics and Management of Beijing University of Posts and Telecommunications, Beijing 100876, China
Evolutionary Game-Based Analysis Of Responses To Hallucinations In Generative Artificial Intelligence, Qiang Yan, Qianyu Zhang, Na Wei
Journal of System Simulation
Abstract: The accelerated deployment of generative artificial intelligence, particularly large language models, has amplified the social risks of hallucinations, posing systemic threats to the credibility of the information ecosystem, the effectiveness of users’ cognitive decision-making, and the governance security in the public domain. Research primarily focuses on hallucination mitigation mechanisms at the technical level or the design of regulatory frameworks at the policy level, lacking a systematic theoretical analysis of the evolutionary logic of strategic interactions among the “large language models, users, and regulators” under conditions of bounded rationality. By introducing evolutionary game theory into the field of generative artificial …
Intelligent Air Combat Decision-Making Method Based On Bigru And Priority Dynamic Sampling,
2026
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Intelligent Air Combat Decision-Making Method Based On Bigru And Priority Dynamic Sampling, Zhengkun Ding, Jiaqi Liu, Junzheng Xu, Yuezhu Xu, Xingmei Wang
Journal of System Simulation
Abstract: Current multi-agent reinforcement learning algorithms suffer from low efficiency in utilizing experience data and difficulties in setting appropriate learning rates. To address these issues, this paper proposed a BiGRU multi-agent PPO with priority sampling and dynamic learning rate. The algorithm incorporated a BiGRU network to enhance the policy network's ability to model temporal information. A priority partial sampling mechanism was introduced to improve the utilization efficiency of high-value experience data. Additionally, an improved Adam optimizer with dynamic learning rate adjustment was employed to address the challenge of learning rate configuration. Simulation experiment results demonstrate that the algorithm significantly …
Knowledge-Enhanced Llm-Based Method For Regional Traffic Signal Control,
2026
School of Artificial Intelligence, Anhui University, Hefei 230601, China
Knowledge-Enhanced Llm-Based Method For Regional Traffic Signal Control, Risheng Xu, Linyao Yang, Yuanqi Qin, Xiao Wang, Changyin Sun
Journal of System Simulation
Abstract: Adaptive traffic signal control (ATSC) is crucial for alleviating regional traffic congestion, yet it faces severe challenges in real-time response to unexpected events and global coordination. The DRL method relies on pure data-driven approaches, suffering from core limitations such as poor generalization, weak interpretability, and a lack of guidance from emergency disposal knowledge, which makes them difficult to meet the demands of complex traffic scenarios. A control system that integrates knowledge-driven and data-optimized approaches was proposed. The GraphRAG was used to construct a dynamic traffic knowledge graph, providing LLMs with real-time updated historical emergency disposal experience and road …
A Semantic Knowledge-Enhanced Assessment Method For Spectrum Effectiveness Of Low Earth Orbit Constellations,
2026
Joint Laboratory for Space Information Network Frequency and Orbit Technology and Applications, China Satellite Network System Research Institute Co., Ltd, Beijing 100000, China
A Semantic Knowledge-Enhanced Assessment Method For Spectrum Effectiveness Of Low Earth Orbit Constellations, Yiqing Liu, Qiuyang Zhang, Chunyu Liu, Yao Xue, Zhiwei Wei, Yan Feng
Journal of System Simulation
Abstract: In order to scientifically assess the spectrum effectiveness of low earth orbit constellations, the problems of insufficient adaptability of the traditional assessment framework and inaccurate estimation of KPIs under sparse data conditions were solved, a semantic knowledge-enhanced assessment method for spectrum effectiveness of low earth orbit constellations was proposed. A comprehensive multi-level, all band, and multi-dimensional spectrum effectiveness assessment framework covering link level, system level, geographical level, and service application level was constructed. A KPI intelligent prediction agent model integrating semantic knowledge and machine learning was proposed to quantify text-like design parameters using SentenceTransformer, so as to rapidly …
Simulation Method For Multi-Crew Construction Processes Based On Large Language Model-Powered Agent,
2026
College of Civil Engineering, Tongji University, Shanghai 200092, China
Simulation Method For Multi-Crew Construction Processes Based On Large Language Model-Powered Agent, Yifan Wang, Bin Yang, Congjun Wang
Journal of System Simulation
Abstract: Traditional construction simulation methods typically rely on predefined rules or static scheduling mechanisms, making it difficult to simulate interactive decision-making under complex constraints by dynamically adapting multi-crew construction scenarios. As a result, workforce idleness caused by process dependencies and spatial occupation fails to be solved. A simulation method for multicrew construction processes based on a LLM-powered agent was proposed. The distributed crew agents endowed with construction scenario understanding and reasoning capabilities were constructed, as well as a centralized project manager agent. A “single-manager and multiple-crew” decision-making mechanism for multi-agent interaction in construction was designed. Autonomous decision-making and coordination mechanism …
Integrated Development Environment For Digital Test Applications Based On Cloud-Edge-End Architecture,
2026
Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China
Integrated Development Environment For Digital Test Applications Based On Cloud-Edge-End Architecture, Wenguang Yu, Qun Li, Hongjie Dang, Hao Chen, Ping Yang
Journal of System Simulation
Abstract: Digital test applications need to be constructed using the unified digital test development tool. After analyzing the features of digital test applications such as large-sample autonomous run, high computational efficiency requirement, and diverse task scenarios, this paper proposes the integrated development environment (IDE) for digital test applications based on cloud-edge-end architecture. The layered expandable architecture, the hybrid integration framework of multi-source heterogeneous models, and the cloud-edge-end collaborative deployment architecture are designed for the IDE of digital test applications. The IDE supports the rapid development, integration, and execution of digital test models and enables development of digital test applications on …
Research On System And Application Framework Of Tactical Wargaming Simulation Driven By Ai4s,
2026
Army Arms University of PLA, Beijing 100072, China; PLA 32302 Troops
Research On System And Application Framework Of Tactical Wargaming Simulation Driven By Ai4s, Dayong Liu, Qisheng Guo, Zhiming Dong, Xuehuan Qiu, Zhuoli Liu
Journal of System Simulation
Abstract: Tactical wargaming simulation, as a crucial tool for combat analysis, simulation training, and equipment demonstration and test, has become a significant means for generating combat effectiveness. Integrating AI into simulation not only enhances simulation efficiency but also diminishes reliance on humans. To assist professionals engaged in tactical wargaming simulation in mastering AI application methods, fostering a systematic mindset, and understanding evolving trends, this paper provided a concise overview of the principles behind AI for science (AI4S). Subsequently, it conducted an analysis of AI4S's application effectiveness in tactical wargaming simulation, established an AI4S-driven wargaming simulation system, and elucidated its composition, …
Knowledge Closed-Loop Driving-Based Intelligent Game Confrontation Simulation,
2026
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha 410073, China
Knowledge Closed-Loop Driving-Based Intelligent Game Confrontation Simulation, Quan Liu, Yu Wang, Linyue Liu, Hao Chen, Jian Huang
Journal of System Simulation
Abstract: For human-machine intelligence integration and collaborative intelligence enhancement, a “knowledge-model-data-knowledge” closed-loop paradigm for combat simulation is proposed to guide the design of a DRL-based game confrontation simulation architecture. By building a combat priori knowledge-guided DRL agent model, mining and analyzing the time series data of agent interactions generated during simulations, and extracting combat posterior knowledge that expands the cognition boundaries of commanders, the knowledge closed-loop driving mechanism for intelligent combat simulations is achieved. The experimental results indicate that the proposed mechanism can effectively endow the combat simulation system with intelligence growth capabilities, providing valuable reference for the deepening …
Resource-Efficient Continuous Learning Framework For Edge Real-Time Video Analytics,
2026
College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China
Resource-Efficient Continuous Learning Framework For Edge Real-Time Video Analytics, Shuxia Wu, Junjie Zhang, Delong Chen, Zheyi Chen
Journal of System Simulation
Abstract: By deploying lightweight models at the network edge, edge systems can provide services of real-time video analytics. However, due to the data drift caused by the discrepancy between model training and actual deployment, it is challenging to construct lightweight models that match real-world environments. To address this challenge, a resource-efficient continuous learning framework for edge real-time video analytics (CL4VA) was proposed. A region of interest-granularity predictor for accuracy degradation was introduced to efficiently select key samples from real-time video streams. A two-layer mixed sample pool was constructed to adaptively trigger the model's continuous learning and avoid the issue of …
Reinforcement Learning Based Method For Uav Team Orienteering Optimization Under Multi-Constraint Condition,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Reinforcement Learning Based Method For Uav Team Orienteering Optimization Under Multi-Constraint Condition, Can Yang, Kai Chen, Feng Zhu
Journal of System Simulation
Abstract: Traditional optimization methods struggle with efficiency, while reinforcement learning approaches often yield low solution quality and high training costs. In response, this paper proposes an attention mechanism-based reinforcement learning method. A dynamic attention strategy network with multi-information fusion is designed to improve solution quality. A visibility-graph approach is employed to simplify threat zone constraints and speed up convergence, and a decoding sequence reordering mechanism is introduced for further performance optimization of the solution. The simulation results show that the method generates high-quality solutions within milliseconds, achieving total rewards that approach or even surpass those obtained by traditional solvers …
Strike Strategy Planning Method Of Unmanned Ground Vehicles Based On Improved Ppo Algorithm,
2026
Northwest Institute of Mechanical & Electrical Engineering, Xianyang 712099, China
Strike Strategy Planning Method Of Unmanned Ground Vehicles Based On Improved Ppo Algorithm, Bingkun Wang, Yue Wang, Mei Yang, Pengnian Zhang, Bohao Fan, Jie Tang
Journal of System Simulation
Abstract: An improved PPO algorithm based on the hybrid action space and gated recurrent unit (GRU) is proposed to address the limitations of predefined strike rules in maximizing the hitting accuracy of unmanned ground vehicles and the difficult coupling and optimization of continuous motion planning and discrete strike decision-making. The environmental model and target model are built for the process of unmanned ground vehicles' strike missions, coupled with a three-layer model for unmanned ground vehicles that fuses kinematic constraints, situational awareness, and dynamic decision-making. Two distinct policy networks are employed, including the continuous motion planning network for path planning, and …
Intelligent Decision-Making Method In Imbalanced Air Combat Based On Asymmetric Self-Play,
2026
School of Software Engineering, Nanchang Hangkong University, Nanchang 330063, China
Intelligent Decision-Making Method In Imbalanced Air Combat Based On Asymmetric Self-Play, Wei Zheng, Jiahao Tang, Xiaoping Xiong, Xin Fan
Journal of System Simulation
Abstract: To solve the problem of strategy convergence caused by role homogenization in traditional self-play for imbalanced air combat, an intelligent decision-making method based on asymmetric selfplay was proposed. This method decoupled tactics from control by employing a hierarchical reinforcement learning framework and designed differentiated reward functions for advantaged and disadvantaged sides. Bidirectional independent policy pools were constructed to promote the co-evolution of strategies. The proximal policy optimization algorithm was utilized to train the model. Experiments in 1v1 weapon-imbalanced and 2v1 numerically-imbalanced scenarios demonstrate that compared to symmetric self-play, the proposed method increases the kill rate of the advantaged …
Agent-Based Pathfinding Method For Indoor Fire Emergency Evacuation,
2026
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Agent-Based Pathfinding Method For Indoor Fire Emergency Evacuation, Ao Tian, Jianqin Zhang, Zheng Wen, Chaonan Hu, Hong Zhao, Bo Shen
Journal of System Simulation
Abstract: To improve emergency evacuation efficiency and reduce casualties in dynamic fire scenarios, a real-time path re-planning method based on agents and dynamic A* algorithm framework is proposed. The behavior and actions of the agent is modeled. A reward function is designed, and an agent-based evacuation framework is constructed. Based on fire simulation data, a dynamic cost network involving parameters such as thermal radiation, smoke visibility, and CO concentration is constructed to achieve spatiotemporally continuous modeling of fire environments. By optimizing the composite cost function through dynamic weight allocation, combined with an improved heuristic function and dynamic search mechanism, local …
Enhancement Of Plastic Deformation In Ultrasound-Assisted Cold Spray Of Tungsten: A Molecular Dynamics Study,
2026
The University of Texas Rio Grande Valley
Enhancement Of Plastic Deformation In Ultrasound-Assisted Cold Spray Of Tungsten: A Molecular Dynamics Study, Md Tusher Ahmed, Farid Ahmed, Jianzhi Li
Manufacturing & Industrial Engineering Faculty Publications
Tungsten (W) is widely valued for its exceptional thermal stability, mechanical strength, and corrosion resistance, making it an ideal candidate for high-performance military and aerospace applications. However, its high melting point and limited room-temperature plasticity pose significant challenges for processing W using additive manufacturing (AM). Cold spray (CS), a solid-state AM process that relies on high-velocity particle impact and plastic deformation, offers a promising route for additive manufacturing of W, yet conventional CS fails to induce sufficient plastic deformation for effective bonding. In this study, we employ atomistic simulations to investigate the effect of ultrasonic perturbation in …
Green Roofs For Grey Water Treatment – Comparative Study Of Economic And Environmental Impacts,
2026
The American University in Cairo AUC
Green Roofs For Grey Water Treatment – Comparative Study Of Economic And Environmental Impacts, Shaden S. Attia
Theses and Dissertations
Cities are under more pressure than ever to use sustainable methods for managing water and other resources because of urbanization, climate change, and a lack of water. Standard greywater treatment systems (SGTSs) work well, but they use a lot of resources and don't provide a lot of ecosystem services. Greywater-treating green roofs (GTGRs) have emerged as a promising Nature-Based Solution (NBS), combining wastewater recycling with benefits like regulating temperature, reducing greenhouse gas (GHG) emissions, and reducing the urban heat island (UHI) effect. Even with this potential, there is still not enough real-world research on how GTGRs affect both the economy …
Artificial Intelligence And Additive Manufacturing As A Coupled Design System: Rethinking Inference, Manufacturability, And Design Education,
2026
The University of Texas Rio Grande Valley
Artificial Intelligence And Additive Manufacturing As A Coupled Design System: Rethinking Inference, Manufacturability, And Design Education, Charul Chadha, Garth Crosby, Sabit Ekin, Mohamed Gharib, Eman Hammad, Congrui Jin, Ali Ahmad Malik, Noemi Mendoza Diaz, Calahan Mollan, Monsuru Ramoni
Manufacturing & Industrial Engineering Faculty Publications
Artificial intelligence (AI) is becoming deeply integrated into additive manufacturing (AM) workflows, reshaping how designers approach geometry, materials, and process constraints. AI holds significant potential by accelerating design exploration, revealing complex patterns in AM behavior, and supporting earlier assessment of manufacturability. At the same time, it introduces new risks related to model transparency, data quality, physical validity, and the potential for overreliance by students and practitioners. This perspective examines these issues through four guiding questions that address the role of AI in AM-enabled design, the gaps that limit or enable AI contribution, the implications for engineering education, and the responsibilities …
Reinforced Scan: A Reinforcement Learning Enabled Optimal Laser Scan Path Planning In Laser Powder Bed Fusion Additive Manufacturing,
2026
University of Missouri
Reinforced Scan: A Reinforcement Learning Enabled Optimal Laser Scan Path Planning In Laser Powder Bed Fusion Additive Manufacturing, Chaoran Dou, Jihoon Chung, Raghav Gnanasambandam, Yuhao Wu, Jianzhi Li, Zhenyu James Kong
Manufacturing & Industrial Engineering Faculty Publications
Additive Manufacturing is an innovative technology that fabricates parts layer by layer. However, in Laser Powder Bed Fusion (LPBF), printed metal parts often exhibit residual stresses, deformations, and other defects due to non-uniform temperature distribution during the printing process. To mitigate these issues, an optimized scan sequence within each layer can improve thermal uniformity. Traditional optimization methods, which rely on domain knowledge and employ trial-and-error or heuristic approaches, often fail to achieve optimal solutions due to the complex nature of the problem. One major challenge in improving scan strategies lies in the vast search space required to optimize the scan …
A Dynamic Inspection And Replacement Policy For Systems Subject To Degradation And Periodic Shocks,
2026
Singapore Management University
A Dynamic Inspection And Replacement Policy For Systems Subject To Degradation And Periodic Shocks, Jiawen Hu, Qiuzhuang Sun
Research Collection College of Integrative Studies
Inspection is widely adopted to improve the maintenance efficiency. Most works predetermine a periodic inspection scheme and focus on optimizing the replacement threshold. However, it is reasonable to inspect less frequently when the degradation level of the system is low and to perform inspections more frequently as the degradation level increases. This work investigates a dynamic inspection and replacement policy for systems subject to regular degradation and periodic shocks. The regular degradation of the system is modeled as a Wiener process. Meanwhile, a periodic arrived shock will incur a random amount increment to the degradation, which is assumed to satisfy …
