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Research Trends of Multi-agent Collaboration Technology for Artificial Intelligence Bots

AI Bots를 위한 멀티에이전트 협업 기술 동향

  • D., Kang ;
  • J.Y., Jung ;
  • C.H., Lee ;
  • M., Park ;
  • J.W., Lee ;
  • Y.J., Lee
  • 강동오 (시각지능연구실) ;
  • 정준영 (시각지능연구실) ;
  • 이천희 (시각지능연구실 ) ;
  • 박민호 (시각지능연구실 ) ;
  • 이전우 (시각지능연구실 ) ;
  • 이용주 (시각지능연구실 )
  • Published : 2022.12.01

Abstract

Recently, decentralized approaches to artificial intelligence (AI) development, such as federated learning are drawing attention as AI development's cost and time inefficiency increase due to explosive data growth and rapid environmental changes. Collaborative AI technology that dynamically organizes collaborative groups between different agents to share data, knowledge, and experience and uses distributed resources to derive enhanced knowledge and analysis models through collaborative learning to solve given problems is an alternative to centralized AI. This article investigates and analyzes recent technologies and applications applicable to the research of multi-agent collaboration of AI bots, which can provide collaborative AI functionality autonomously.

Keywords

Acknowledgement

이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임[No. 2022-0-00907, (2세부) AI Bots 협업 플랫폼 및 자기조직 인공지능 기술개발].

References

  1. Google AI Blog, Federated Learning: Collaborative Machine Learning without Centralized Training Data, 2017, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html 
  2. 박준희 외, "IoT기반 초연결 공간 분산지능 기술," 전자통신동향분석, 제33권 제1호, 2018. 
  3. L.E. Parker, "Distributed Intelligence: Overview of the field and its application in multi-robot systems," in AAAI Fall Symposium: Regarding the Intelligence in Distributed Intelligent Systems, AAAI Press, Menlo Park, CA, USA, 2007, pp. 1-6. 
  4. M. Biswas, Beginning AI Bot Frameworks: Getting Started with Bot Development, Apress Berkeley, CA, USA, 2018. 
  5. J. Duan et al., "A survey of embodied AI: From simulators to research tasks," IEEE Trans. Emerg. Top. Comput. Intell., vol. 6, no. 2, 2022. 
  6. https://servisbot.com/5-use-cases-for-multi-bot-architecture/ 
  7. IITP, 인공지능 기술 청사진 2030, 2020. 12. 
  8. W. Guo, J. Wang, and S. Wang, "Deep multimodal representation learning: A survey," IEEE Access, vol. 7, 2021, pp. 63373-63394.  https://doi.org/10.1109/access.2019.2916887
  9. A. Baheti and S. Bhokre, Additive Learning Framework for Self Evolving AI, NVIDIA, 2018. 
  10. T. Cieslewski, S. Choudhary, and D. Scaramuzza, "Data-efficient decentralized visual SLAM," in Proc. IEEE Int. Conf. Robot. Autom., (Brisbane, Australia), May 2018, pp. 2466-2473. 
  11. P-Y. Lajoie et al., "DOOR-SLAM: Distributed, online, and outlier resilient SLAM for robotic teams," IEEE Robot. Autom. Lett., vol. 5, no. 2, 2020. 
  12. M. Kegeleirs, G. Giorgio, and M. Birattari, "Swarm SLAM: Challenges and perspectives," Front. Robot. AI, vol. 8, 2021. 
  13. Y. Chang et al., "Kimera-multi: A system for distributed multi-robot metric-semantic simultaneous localization and mapping," in Proc. IEEE Int. Conf. Robot. Autom., (Xi'an, China), May 2021, pp. 11210-11218. 
  14. U. Jain et al., "Two body problem: Collaborative visual task completion," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., (Long Beach, CA, USA), June 2019, pp. 6689-6699. 
  15. X. Liu et al., "Multi-agent embodied visual semantic navigation with scene prior knowledge," arXiv preprint, CoRR, 2021, aXiv: 2109.09531. 
  16. Y. Rizk et al., "Cooperative heterogeneous multi-robot systems: A survey," ACM Comput. Surv., vol. 52, no. 2, 2019. 
  17. U. Unnat et al., "A cordial sync: Going beyond marginal policies for multi-agent embodied tasks," in European Conference on Computer Vision, Springer, Cham, Switzerland, 2020, pp. 471-490. 
  18. G.T. Papadopoulos e t al., "Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning," IEEE Access, vol. 9, 2021. 
  19. R. Bernardo et al., "Planning robotic agent actions using semantic knowledge for a home environment," Intell. Robot., vol. 1, no. 2, 2021. 
  20. K. Wang et al., "Visual semantic planning for service robot via natural language instructions," in Proc. IEEE China Autom. Congr. (CAC), (Beijing, China), Oct. 2021, pp. 793-798. 
  21. J. Moon, "Plugin framework-based neuro-symbolic grounded task planning for multi-agent system," Sensors, vol. 21, no. 23, 2021. 
  22. J. Francis et al., "Core challenges in embodied vision-language planning," J. Artif. Intell. Res., 2022. 
  23. https://askforalfred.com/EAI22/ 
  24. S. Tan et al., "Multi-agent embodied question answering in interactive environments," in European Conference on Computer Vision, Springer, Cham, Switzerland, 2020, pp. 663-678. 
  25. T.T. Nguyen et al., "Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications," IEEE Trans. Cybern., vol. 50, no. 9, 2020. 
  26. L. Espeholt et al., "Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures," in Proc. Int. Conf. Mach. Learn., (Stockholm, Sweden), Jul. 2018, pp. 1407-1416. 
  27. H. Mao et al., "Learning agent communication under limited bandwidth by message pruning," in Proc. AAAI Conf. Artif. Intell., (New York, NY, USA), Feb. 2020, pp. 5142-5149. 
  28. E. Hejazi, "Multi-agent machine learning in self-organizing systems," Inf. Sci., vol. 581, 2021. 
  29. S. Reddi et al., "Adaptive federated optimization," Int. Conf. Learn. Repersentation (ICLR), (Virtual Only), May 2021. 
  30. A.G. Roy et al., "Braintorrent: A peer-to-peer environment for decentralized federated learning," arXiv preprint, CoRR, 2019, arXiv: 1905.06731. 
  31. T. Winak and Z. Nochta, "An approach for peer-to-peer federated learning," in Proc. Ann. IEEE/IFIP Inter. Conf. Dependable Syst. Netw. Workshops (DSN-W), (Taipei, Taiwan), June 2021, pp. 150-157. 
  32. https://echord.eu/saga.html 
  33. 한경수, 정훈, "드론 물류 배송 서비스 동향," 전자통신동향분석, 제35권 제1호, 2020, pp. 71-79.  https://doi.org/10.22648/ETRI.2020.J.350107
  34. 삼정KPMG 경제연구원, "하늘 위에 펼쳐지는 모빌리티 혁명, 도심 항공 모빌리티," 삼정 인사이트, 통권 제70호, 2020. 
  35. https://en.wikipedia.org/wiki/Amazon_Robotics 
  36. https://magazine.hankyung.com/business/article/202103114564b 
  37. https://www.amazon.science/latest-news/amazonrobotics-see-robin-robot-arms-in-action