References
- Reddy, R., Chopra, S., Singh, M.: Leveraging Transformer Models and Reinforcement Learning for Optimized AI-Enhanced Automated Sales Outreach. Innovative AI Research Journal, 12(1) (2023). Available at:
https://www.iairj.com/index.php/v1/article/view/7 - Zou, D., Wei, W., Zhu, F., Xu, C., Zhang, T., Huo, C.: Knowledge Enhanced Multi-intent Transformer Network for Recommendation. arXiv preprint arXiv:2405.20565 (2024). Available at:
https://arxiv.org/abs/2405.20565 - A. Goeckner, Y. Sui, N. Martinet, X. Li, and Q. Zhu, “Graph Neural Network based Multi-agent Reinforcement Learning for Resilient Distributed Coordination of Multi-Robot Systems,” arXiv preprint arXiv:2403.13093, 2024. Available:
https://arxiv.org/abs/2403.13093 - Qin, Z., Lu, Y.: Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing. Journal of Intelligent Manufacturing (2024). Published 13 October 2024. Available at: DOI: 10.1007/s10845-024-02494-0
- Y. Chen, J. Liu, Z. Li, K. Zhang, and S. Zhang, “LLM-Agents: Large Language Model based Multi-Agent Reinforcement Learning,” arXiv preprint arXiv:2404.04898, 2024. Available:
https://arxiv.org/abs/2404.04898 - Y. Ma, O. Burns, M. Wang, G. Li, N. Du, L. ElShafey, L. Wang, I. Shafran, and H. Soltau, “Knowledge Graph Reasoning with Self-supervised Reinforcement Learning,” arXiv preprint arXiv:2405.13640, 2024. Available:
https://arxiv.org/abs/2405.13640 - McCrae, J., Manjunath, S.: Intent Classification by the Use of Automatically Generated Knowledge Graphs. Information, 14(5), Article 288 (2023). Available at: DOI: 10.3390/info14050288
- Z. Wang, B. Wang, H. Jing, H. Li, and H. Dou, “Walk Wisely on Graph: Knowledge Graph Reasoning with Dual Agents via Efficient Guidance-Exploration,” arXiv preprint arXiv:2408.01880, 2024. Available:
https://arxiv.org/abs/2408.01880 - Zhou, F., Mi, J., Zhang, B., Shi, J., Zhang, R., Chen, X., Zhao, Y., Zhang, J.: Reliable knowledge graph fact prediction via reinforcement learning. Visual Computing for Industry, Biomedicine, and Art, 6 (Article 21), (2023). Published 20 November 2023. Available at: DOI: 10.1186/s42492-023-00150-7
- M. Saebi, S. Krieg, C. Zhang, M. Jiang, and N. Chawla, “Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning,” arXiv preprint arXiv:2003.06050, 2020. Available:
https://arxiv.org/abs/2003.06050 - N. Quach, Q. Wang, Z. Gao, Q. Sun, B. Guan, and L. Floyd, “Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs,” arXiv preprint arXiv:2404.12587, 2024. Available:
https://arxiv.org/abs/2404.12587 - Tao, S., Qiu, R., Cao, Y., Xue, G., Ping, Y.: Path-guided intelligent switching over knowledge graphs with deep reinforcement learning for recommendation. Complex and Intelligent Systems, 9, 7305–7319 (2023). Published 29 June 2023. Available at: DOI: 10.1007/s40747-023-01124-1
- Yu, C., Wang, W., Liu, X., Bai, J., Song, Y., Li, Z., Gao, Y., Cao, T., Yin, B.: FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery. arXiv preprint arXiv:2211.08316 (2023). Available at:
https://arxiv.org/abs/2211.08316 - C. Hao, W. Feng, Y. Zhang, and H. Wang, “DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning,” arXiv preprint arXiv:2507.17365, July 2025. Available:
https://arxiv.org/abs/2507.17365 - Zhang, X., Li, Y., Wang, J., Chen, H., Liu, Z.: Knowledge graph-enhanced multiagent reinforcement learning for adaptive scheduling in smart manufacturing. Journal of Intelligent Manufacturing, (2024). Available at: DOI: 10.1007/s10845-024-02494-0
- Wang, Y., Zhao, L., Chen, X., Sun, J., Xu, H.: Knowledge graph driven credit risk assessment for micro, small and medium-sized enterprises. International Journal of Production Research, (2023). Available at: DOI: 10.1080/00207543.2023.2257807
- Tang, R., Yu, Z., Chen, Q., Zhang, M.: KGLA: Knowledge Graph Enhanced Language Agents for Recommendation. arXiv preprint, arXiv:2410.19627 (2024). Available at:
https://arxiv.org/pdf/2410.19627 - Li, H., Wang, Y., Zhou, J., Zhang, K.: GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases. arXiv preprint, arXiv:2504.05478 (2025). Available at:
https://arxiv.org/pdf/2504.05478v1 - Ma, Y., Xu, H., Chen, D., Zhang, J.: LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering. arXiv preprint, arXiv:2406.06621 (2024). Available at:
https://arxiv.org/pdf/2406.06621 - Shen, Z., Luo, P., Yang, L., Wang, Y.: Knowledge Graph Tuning: Real-time Large LLM Personalization based on Feedback. arXiv preprint, arXiv:2405.19686 (2024). Available at:
https://arxiv.org/pdf/2405.19686 - Liu, H., Zhang, Y., Xu, M., Zhao, X.: Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI. IEEE Transactions on Neural Networks and Learning Systems, (2024). Available at:
https://iee-explore.ieee.org/document/10531671 - Zhou, L., Chen, Y., Wu, H., Li, F.: Enhancing Multi-Agent Systems via RL with LLM-based Planner & Graph-based Policy. arXiv preprint, arXiv:2503.10049 (2025). Available at:
https://arxiv.org/pdf/2503.10049 - Rahman, M., Ahmed, S., Chowdhury, N.: Multi-agent learning and negotiation in e-market environment. 2022 IEEE International Conference on Artificial Intelligence and Education (AIE), pp. 1–6. Available at: DOI: 10.1109/AIE57029.2022.00083
- Liu, Q., Chen, T., Huang, W., Jiang, Z.: Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance. Proceedings of the ACM on Knowledge Discovery and Data Mining, (2023). Available at:
https://doi.org/10.1145/3580305.3599856 - Wu, D., Cai, Y., Chen, H.: Multi-agent collaboration for B2B workflow monitoring. Knowledge-Based Systems, 15(8), 493–502 (2002). Available at:
https://doi.org/10.1016/S0950-7051(02)00033-3 - Gao, L., Xu, T., Li, H., Zhang, Q.: Multi-Agent Deep RL platform for real-time dynamic control of power systems. Neural Computing and Applications, (2024). Available at: DOI: 10.1007/s00521-024-10488-5
- Feng, J., Liu, R., Zhao, T., Li, P.: Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM. arXiv preprint, arXiv:2507.02170 (2025). Available at:
https://www.arxiv.org/pdf/2507.02170v1 - Popescu, E., Morar, A., Mihaiu, G.: Multi-Agent Recommendation and Aspect Level Sentiment Analysis in B2B CRM Systems. 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 293–300. Available at: DOI: 10.1109/SYNASC51798.2020.00046
- Zhang, Y., Li, H., Chen, M.: Multi-Agent Deep RL for Cloud-Based Digital Twins in Power Grid Management. Journal of Cloud Computing, 13, Article 713 (2024). Available at:
https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-024-00713-w - Wang, Q., Zhao, L., Xu, Y.: Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent RL. arXiv preprint, arXiv:2403.19253 (2024). Available at:
https://arxiv.org/pdf/2403.19253 - Gupta, R., Sharma, A., Singh, P.: Content Caching-Assisted Vehicular Edge Computing Using Multi-Agent Graph Attention RL. arXiv preprint, arXiv:2410.10071 (2024). Available at:
https://arxiv.org/pdf/2410.10071 - Huang, X., Liu, T., Zhou, K.: AGENTSNET: Coordination & Collaborative Reasoning in Multi-Agent LLMs. arXiv preprint, arXiv:2507.08616 (2025). Available at:
https://arxiv.org/pdf/2507.08616 - Hernandez-Leal, P., Kartal, B., Taylor, M.E.: Multi-Agent Reinforcement Learning: A Comprehensive Overview. arXiv preprint, arXiv:2312.10256 (2023). Available at: DOI: 10.48550/arXiv.2312.10256
- Jiang, J., Lu, Z.: Graph Convolutional Reinforcement Learning. arXiv preprint, arXiv:1810.09202 (2018). Available at: DOI: 10.48550/arXiv.1810.09202
- Zhou, D., Li, W., Chen, X.: Fit for Purpose: Modeling Wholesale Electricity Markets Realistically with Multi-Agent Deep Reinforcement Learning. Engineering Applications of Artificial Intelligence, Vol. 126, 100295 (2023). Available at: DOI: 10.1016/j.egyai.2023.100295
- Tesauro, G., Kephart, J.O.: Learning Competitive Pricing Strategies by MultiAgent Reinforcement Learning. Journal of Economic Dynamics & Control, 27(6), 893–921 (2003). Available at: DOI: 10.1016/S0165-1889(02)00122-7
- Iqbal, S., Sha, F.: Intent-Aware Multi-Agent Reinforcement Learning. IEEE International Conference on Robotics and Automation (ICRA), pp. 7444–7451 (2018). Available at: DOI: 10.1109/ICRA.2018.8463211
- Brown, A., Kumar, S.: Adaptive Marketing Campaigns Using Deep Reinforcement Learning: A Customer-Centric Approach. Frontiers in Business, Finance & Technology, Vol. 5 (2023). Available at: DOI: 10.71465/fbf254
- Chen, R., Wang, L., Xu, T.: Multi-Task Multi-Agent Reinforcement Learning via Skill Graphs. arXiv preprint, arXiv:2507.06690 (2025). Available at:
https://arxiv.org/pdf/2507.06690v1 - Singh, V., Zhao, K., Lee, H.: Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Condition. arXiv preprint, arXiv:2507.02698 (2025). Available at: DOI: 10.48550/arXiv.2507.02698
- Patel, N., Mehta, R., Zhang, Y.: Multi-Agent Reinforcement Learning for Job Shop Scheduling with GNN State Representation. Sustainability, 16(8), 3234 (2024). Available at: DOI: 10.3390/su16083234
- Yang, F., Zhao, H., Li, P.: A Survey on Multi-Agent Reinforcement Learning and its Applications. Journal of Artificial Intelligence, Vol. 6, 100020 (2024). Available at: DOI: 10.1016/j.jai.2024.02.003
- Cho, G., Shim, P.-s., Kim, J.: Explainable B2B Recommender System for Potential Customer Prediction Using KGAT. Electronics 12(17):3536 (2023). Available at: DOI: 10.3390/electronics12173536
- Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving Factuality and Reasoning in Language Models through Multiagent Debate. arXiv preprint arXiv:2305.14325 (2023). Available at:
https://arxiv.org/abs/2305.14325 - Zhou, Y., Zanette, A., Pan, J., Levine, S., Kumar, A.: ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL. arXiv preprint arXiv:2402.19446 (2024). Available at:
https://arxiv.org/abs/2402.19446 - Patil, S., Vaze, V., Agarkar, P., Mahajan, H.: Social context-aware and fuzzy preference temporal graph for personalized B2B marketing campaigns recommendations. Soft Computing (2023). Available at: DOI: 10.1007/s00500-023-08914-2
- Henna, S., Kalliadan, S.K., Amjath, M.: Optimizing B2B customer relationship management and sales forecasting with spectral graph convolutional networks: A quantitative approach. Quantitative Finance and Economics 9(2):449–478 (2025). Available at: DOI: 10.3934/QFE.2025015
- Sankaradas, M., Rajendran, R.K., Chakradhar, S.T.: StreamingRAG: Realtime Contextual Retrieval and Generation Framework. arXiv preprint arXiv:2501.14101 (2025). Available at:
https://arxiv.org/abs/2501.14101 - Wang, X., Huang, T., Wang, D., Yuan, Y., Liu, Z., He, X., Chua, T.-S.: Learning Intents behind Interactions with Knowledge Graph for Recommendation (KGIN). arXiv preprint arXiv:2102.07057 (2021). Available at:
https://arxiv.org/abs/2102.07057 - Ozsoy, M.G., Messallem, L., Besga, J., Minneci, G.: Text2Cypher: Bridging Natural Language and Graph Databases. arXiv preprint arXiv:2412.10064 (2024). Available at:
https://arxiv.org/abs/2412.10064 - Yip, H.Y., Liu, Y., Sheth, A.: Using Contact, Content, and Context in Knowledge Infused Learning: A Case Study of Non-Sequential Sales Processes in Sales Engagement Graphs. In: Proceedings of the Knowledge-Infused Learning (K-IL) Workshop at the Knowledge Graph Conference 2021, CEUR-WS.org, pp. 1–8 (2021). Available at:
https://aiisc.ai/KiL2021/papers/K-iL2021 paper 2.pdf - Y. M. Brovman, Building a Deep Learning Based Retrieval System for Personalized Recommendations, eBay Innovation (2022). [Online]. Available:
https://innovation.ebayinc.com/stories/building-a-deep-learning-based-retrieval-system-for-personalized-recommendations/ . - Changlong Yu and Zheng Li, Building commonsense knowledge graphs to aid product recommendation, Amazon Science Blog, May 10, 2024. Available:
https://www.amazon.science/blog/building-commonsense-knowledge-graphs-to-aid-product-recommendation - Tianmin Shu and Yuandong Tian, M3RL: Mind-aware Multi-agent Management Reinforcement Learning, Published as a conference paper at ICLR 2019.
https://scontent.fpnq7-5.fna.fbcdn.net/v/t39.8562-6/24084555210107969030479723760881129737168841n.pdf?nccat=107&ccb=1-7&ncsid=e280be&ncohc=WjQVdPoDXEcQ7kNvwG-ZYwL3&ncoc=AdkyLfkAJfXKdLn1g5egqPE5CXMs-bLSmqJMr9hooE0JRuyaIo3w1Q5mdR9dKityVpOQ&nczt=14&ncht=scontent.fpnq7-5.fna&ncgid=c-QCSRfsxg7N6bwNTIhnxA&oh=00AfVl2PPmEjxSVTv-05vHrkR1PtxM-i3t7jqL6oAbgwPOGw&oe=68B06DD9 - Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, and Yinghui Xu, Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application, Proceedings of KDD ’18, 2018. DOI: 10.1145/3219819.3219846.
https://arxiv.org/pdf/1803.00710 - Jannuzzi, M., Perezhoh in, Y., Peres, F., Castelli, M., & Popovič, A. (2024). Zero-Shot Prompting Strategies for Table Question Answering with a Low-Resource Language. Emerging Science Journal, 8(5), 2003–2022. DOI: 10.28991/ESJ-2024-08-05-020
- Editya, A. S., Ahmad, T., & Studiawan, H. (2024). Visual Instruction Tuning for Drone Accident Forensics. HighTech and Innovation Journal, 5(4), 870–884. DOI: 10.28991/HIJ-2024-05-04-01