INTELLIGENT AGENT SYSTEMS FOR ADAPTIVE DECISION MAKING IN LARGE SCALE SMART ENVIRONMENTS
Malaysia Malaysia 
Indonesia 
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Adaptive Decision-Making, Intelligent Agent Systems, Real-Time Systems, Smart Environments, Scalability
Abstract
The rapid growth of large-scale smart environments, including smart cities, autonomous transportation systems, and smart grids, has necessitated advanced decision-making mechanisms capable of adapting to dynamic and complex conditions. Intelligent agent systems (IAS) offer a promising solution by enabling decentralized, autonomous decision-making based on real-time data. This study explores the application of IAS for adaptive decision-making in large-scale smart environments, focusing on the challenges of scalability, resource allocation, and system responsiveness. The primary objective is to design and evaluate an intelligent agent system capable of operating efficiently in diverse, complex environments. A mixed-methods approach was used, combining simulations and real-world implementations in various smart environments, including energy grids, smart cities, and industrial automation systems. The results indicate that while IAS can perform effectively in smaller environments, performance decreases in large-scale systems due to increased agent interaction and data complexity. Scalability and adaptability remain significant challenges, with response times and resource allocation efficiency declining as the system size grows. The study concludes that further advancements are required in communication protocols and machine learning algorithms to enhance the scalability and real-time decision-making of IAS in large, interconnected systems.
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References
Du, Z., Luo, Chunbo, Min, G., Wu, J., Luo, Cai, Pu, J., & Li, S. (2025). A Survey on Autonomous and Intelligent Swarms of Uncrewed Aerial Vehicles (UAVs). IEEE Transactions on Intelligent Transportation Systems, 26(10), 14477–14500. https://doi.org/10.1109/TITS.2025.3569500
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gaševi?, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Guo, S., Wang, Y., Zhang, N., Su, Z., Luan, T. H., Tian, Z., & Shen, X. (2025). A Survey on Semantic Communication Networks: Architecture, Security, and Privacy. IEEE Communications Surveys & Tutorials, 27(5), 2860–2894. https://doi.org/10.1109/COMST.2024.3516819
Huang, J., Wu, B., Duan, Q., Dong, L., & Yu, S. (2025). A Fast UAV Trajectory Planning Framework in RIS-Assisted Communication Systems With Accelerated Learning via Multithreading and Federating. IEEE Transactions on Mobile Computing, 24(8), 6870–6885. https://doi.org/10.1109/TMC.2025.3544903
Hughes, L., Dwivedi, Y. K., Malik, T., Shawosh, M., Albashrawi, M. A., Jeon, I., Dutot, V., Appanderanda, M., Crick, T., De’, R., Fenwick, M., Gunaratnege, S. M., Jurcys, P., Kar, A. K., Kshetri, N., Li, K., Mutasa, S., Samothrakis, S., Wade, M., & Walton, P. (2025). AI Agents and Agentic Systems: A Multi-Expert Analysis. Journal of Computer Information Systems, 65(4), 489–517. https://doi.org/10.1080/08874417.2025.2483832
Li, Dezhen, Yin, J., Yu, Z., Gao, Z., Xu, N., & Meng, L. (2025). Artificial intelligence-assisted colorimetric sensor array based on supramolecular self-assembled nanozymes for visual monitoring of pesticide residues. Sensors and Actuators B: Chemical, 444, 138493. https://doi.org/10.1016/j.snb.2025.138493
Li, Jialiuyuan, Shi, Y., Dai, C., Yi, C., Yang, Y., Zhai, X., & Zhu, K. (2025). A Learning-Based Stochastic Game for Energy Efficient Optimization of UAV Trajectory and Task Offloading in Space/Aerial Edge Computing. IEEE Transactions on Vehicular Technology, 74(6), 9717–9733. https://doi.org/10.1109/TVT.2025.3540964
Li, Jialiuyuan, Yi, C., Chen, J., Shi, Y., Zhang, T., Li, X., Wang, R., & Zhu, K. (2025). A Reinforcement Learning-Based Stochastic Game for Energy-Efficient UAV Swarm-Assisted MEC With Dynamic Clustering and Scheduling. IEEE Transactions on Green Communications and Networking, 9(1), 255–270. https://doi.org/10.1109/TGCN.2024.3424449
Li, Zhengyang, Ji, Q., Ling, X., & Liu, Q. (2025). A Comprehensive Review of Multiagent Reinforcement Learning in Video Games. IEEE Transactions on Games, 17(4), 873–892. https://doi.org/10.1109/TG.2025.3588809
Li, Zhenning, Jiang, H., & Dong, Y. (2025). A convolutional-transformer reinforcement learning agent for rotating machinery fault diagnosis. Expert Systems with Applications, 271, 126669. https://doi.org/10.1016/j.eswa.2025.126669
Liu, Jinzhuo, Jiang, G., Chu, C., Li, Y., Wang, Z., & Hu, S. (2025). A formal model for multiagent Q-learning on graphs. Science China Information Sciences, 68(9), 192206. https://doi.org/10.1007/s11432-024-4289-6
Liu, Yang, Chen, W., Bai, Y., Liang, X., Li, G., Gao, W., & Lin, L. (2025). Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI. IEEE/ASME Transactions on Mechatronics, 30(6), 7253–7274. https://doi.org/10.1109/TMECH.2025.3574943
Liu, Youshan, Fan, J., & Shen, W. (2025). A deep reinforcement learning approach with graph attention network and multi-signal differential reward for dynamic hybrid flow shop scheduling problem. Journal of Manufacturing Systems, 80, 643–661. https://doi.org/10.1016/j.jmsy.2025.03.028
Ma, S., He, J., Chen, Q., Zhou, J., Xie, F., & Cai, J. (2025). Advancements in starch-based nanomaterials for functional delivery and food packaging: A comprehensive review and future directions. Journal of Future Foods, 5(5), 443–454. https://doi.org/10.1016/j.jfutfo.2024.08.002
Mbungu, N. T., Siti, M. W., Bansal, R. C., Naidoo, R. M., Elnady, A., Ismail, A. A. A., Abokhali, A. G., & Hamid, A.-K. (2025). A dynamic coordination of microgrids. Applied Energy, 377, 124486. https://doi.org/10.1016/j.apenergy.2024.124486
Min, H., Rahmani, A. M., Ghaderkourehpaz, P., Moghaddasi, K., & Hosseinzadeh, M. (2025). A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks. Ad Hoc Networks, 166, 103656. https://doi.org/10.1016/j.adhoc.2024.103656
Moreira, R., Villaça, R. S., Ribeiro, M. R. N., Martins, J. S. B., Corrêa, J. H., Carvalho, T. C., & De Oliveira Silva, F. (2025). An intelligent native network slicing security architecture empowered by federated learning. Future Generation Computer Systems, 163, 107537. https://doi.org/10.1016/j.future.2024.107537
Niu, B., Gao, Y., Zhang, G., Zhao, X., Wang, H., Wang, D., & Liu, C. (2025). Adaptive Prescribed-Time Consensus Tracking Control Scheme of Nonlinear Multi-Agent Systems Under Deception Attacks. IEEE Transactions on Automation Science and Engineering, 22, 4196–4205. https://doi.org/10.1109/TASE.2024.3408453
Niu, B., Shang, Z., Zhang, G., Chen, W., Wang, H., Zhao, X., & Wang, D. (2025). Adaptive Fuzzy Resilient Fixed-Time Bipartite Consensus Tracking Control for Nonlinear MASs Under Sensor Deception Attacks. IEEE Transactions on Automation Science and Engineering, 22, 2542–2551. https://doi.org/10.1109/TASE.2024.3381568
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026). AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges. Information Fusion, 126, 103599. https://doi.org/10.1016/j.inffus.2025.103599
Shadkam, E., & Irannezhad, E. (2025). A comprehensive review of simulation optimization methods in agricultural supply chains and transition towards an agent-based intelligent digital framework for agriculture 4.0. Engineering Applications of Artificial Intelligence, 143, 109930. https://doi.org/10.1016/j.engappai.2024.109930
Singh, A. R., Sujatha, M. S., Kadu, A. D., Bajaj, M., Addis, H. K., & Sarada, K. (2025). A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems. Scientific Reports, 15(1), 19309. https://doi.org/10.1038/s41598-025-02649-w
Song, B., Shen, J., Shi, X., Zhou, J., & Gu, H. (2025). Antibacterial, Antifreezing, and Tough Electronic Skin Based on a Tanned Collagen Fiber Network for Underwater Grabber Application. ACS Applied Materials & Interfaces, 17(27), 39561–39575. https://doi.org/10.1021/acsami.5c11788
Song, Y., Sun, W., Shi, X., Qin, Z., Wu, Q., Yin, S., Liang, S., Liu, Z., & Sun, H. (2025). Bio-inspired e-skin with integrated antifouling and comfortable wearing for self-powered motion monitoring and ultra-long-range human-machine interaction. Journal of Colloid and Interface Science, 679, 1299–1310. https://doi.org/10.1016/j.jcis.2024.10.056
Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Qiu, J., Xu, J., Ding, M., Li, H., Geng, M., Wu, Yue, Wang, W., Chen, J., Yin, Z., Ren, X., Fu, J., He, J., Wu, Yuan, Liu, Qi, … Li, Z. (2025). A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook. ACM Computing Surveys, 3729218. https://doi.org/10.1145/3729218
Tejada, J. C., Toro-Ossaba, A., López-Gonzalez, A., Hernandez-Martinez, E. G., & Sanin-Villa, D. (2025). A Review of Multi-Robot Systems and Soft Robotics: Challenges and Opportunities. Sensors, 25(5), 1353. https://doi.org/10.3390/s25051353
Vu, M. T., Kim, S. H., Pham, D. H., Thanh, H. L. N. N., Pham, V. H., & Roohi, M. (2025). Adaptive Dynamic Programming-Based Intelligent Finite-Time Flexible SMC for Stabilizing Fractional-Order Four-Wing Chaotic Systems. Mathematics, 13(13), 2078. https://doi.org/10.3390/math13132078
Wang, B., Tian, Z., Tang, F., Pan, H., She, W., & Liu, W. (2025). Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction. IEEE Internet of Things Journal, 12(11), 17095–17109. https://doi.org/10.1109/JIOT.2025.3538887
Xu, Y., Xu, X., Zhao, Y., Tian, Y., Ma, Y., Zhang, X., Li, F., Zhao, W., Ma, J., Xu, Q., & Sun, Q. (2025). A self-powered casein hydrogel E-dressing with synergistic photothermal therapy, electrical stimulation, and antibacterial effects for chronic wound management. Acta Biomaterialia, 198, 63–84. https://doi.org/10.1016/j.actbio.2025.03.047
Yang, T., Feng, P., Guo, Q., Zhang, J., Zhang, X., Ning, J., Wang, X., & Mao, Z. (2025). AutoHMA-LLM: Efficient Task Coordination and Execution in Heterogeneous Multi-Agent Systems Using Hybrid Large Language Models. IEEE Transactions on Cognitive Communications and Networking, 11(2), 987–998. https://doi.org/10.1109/TCCN.2025.3528892
Yuan, J., Lei, Y., Li, N., Yang, B., Li, X., Chen, Z., & Han, W. (2025). A framework for modeling and optimization of mechanical equipment considering maintenance cost and dynamic reliability via deep reinforcement learning. Reliability Engineering & System Safety, 264, 111424. https://doi.org/10.1016/j.ress.2025.111424
Zhou, W., Zhao, Y., Zhang, Y., Wang, Y., & Yin, M. (2025). A comprehensive survey of UPPAAL?assisted formal modeling and verification. Software: Practice and Experience, 55(2), 272–297. https://doi.org/10.1002/spe.3372
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Copyright (c) 2026 Aiman Fariq, Nina Anis, Mirza Ilhami
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