Lai Y, Kankanhalli A, Ong D. Human-AI collaboration in healthcare: a review and research agenda. In: Hawaii International Conference on System Sciences, Virtual, pp. 1–10 (2021)

Kim Y, Park C, Jeong H, Chan YS, Xu X, McDuff D, Lee H, Ghassemi M, Breazeal C, Park HW. MDAgents: an adaptive collaboration of LLMs for medical decision-making. In: Advances in Neural Information Processing Systems, Vancouver, BC, Canada, pp. 79410–79452 (2024)

Vaccaro M, Almaatouq A, Malone T. When combinations of humans and AI are useful: a systematic review and meta-analysis. Nat Hum Behaviour. 2024;8:2293–303.

Article  Google Scholar 

Peng S, Wang MX, Shah JA, Figueroa N. Object permanence filter for robust tracking with interactive robots. In: IEEE International Conference on Robotics and Automation, Yokohama, Japan, pp. 4909–4915 (2024)

Alrashedy K, Alrashedy K, Tambwekar P, Zaidi ZH, Langwasser M, Xu W, Gombolay M. Object permanence filter for robust tracking with interactive robots. In: International Conference on Learning Representations, Singapore City, Singapore, pp. 1–27 (2025)

Ashktorab Z, Liao QV, Dugan C, Johnson J, Pan Q, Zhang W, Kumaravel S, Campbell M. Human-AI collaboration in a cooperative game setting: measuring social perception and outcomes. In: Proceedings of the ACM on Human-Computer Interaction, New York City, NY, USA, pp. 1–20 (2020)

Chang E, Chen Z, Labrune J, Coelho M. Be the Beat: AI-powered boombox for music suggestion from freestyle dance. In: International Conference on Tangible, Embedded, and Embodied Interaction, New York City, NY, USA, pp. 1–6 (2025)

Guo G, Kumar AMS, Gupta A, Coscia A, Maclellan C, Endert A. Visualizing intelligent tutor interactions for responsive pedagogy. In: International Conference on Advanced Visual Interfaces, New York City, NY, USA, pp. 1–9 (2024)

Shen H, Knearem T, Ghosh R, Alkiek K, Krishna K, Liu Y, Ma Z, Petridis S, Peng Y-H, Qiwei L, Rakshit S, Si C, Xie Y, Bigham JP, Bentley F, Chai J, Lipton Z, Mei Q, Mihalcea R, Terry M, Yang D, Morris MR, Resnick P, Jurgens D. Towards bidirectional human-AI alignment: a systematic review for clarifications, framework, and future directions. arXiv:. (2024)

Nalepka P, Lamb M, Kallen RW, Shockley K, Chemero A, Saltzman E, et al. Human social motor solutions for human–machine interaction in dynamical task contexts. Proceed National Academy Sci. 2019;116(4):1437–46.

Article  Google Scholar 

Chanel CPC, Roy RN, Dehais F, Drougard N. Towards mixed-initiative human-robot interaction: assessment of discriminative physiological and behavioral features for performance prediction. Sensors. 2020;20(1):1–20.

Article  Google Scholar 

Zuo G, Tong J, Wang Z, Gong D. A graph-based deep reinforcement learning approach to grasping fully occluded objects. Cognit Comput. 2023;15(1):36–49.

Article  Google Scholar 

Li W, Liu W, Shao S, Huang S, Song A. Attention-based intrinsic reward mixing network for credit assignment in multiagent reinforcement learning. IEEE Trans Games. 2024;16(2):270–81.

Article  Google Scholar 

Schilling M, Hammer B, Ohl FW, Ritter HJ, Wiskott L. Modularity in nervous systems-a key to efficient adaptivity for deep reinforcement learning. Cognit Comput. 2024;16(5):2358–73.

Article  Google Scholar 

Ni Z, Jin Y, Liu P, Zhao W. A novel heuristic exploration method based on action effectiveness constraints to relieve loop enhancement effect in reinforcement learning with sparse rewards. Cognit Comput. 2024;16(2):682–700.

Article  Google Scholar 

Ghadirzadeh A, Chen X, Yin W, Yi Z, Bjorkman M, Kragic D. Human-centered collaborative robots with deep reinforcement learning. IEEE Robot Automat Lett. 2020;6:566–71.

Article  Google Scholar 

Dijkstra EB. Adaptive reinforcement learning for human-AI collaboration. arXiv:. (2022)

Bucinca Z, Swaroop S, Paluch AE, Murphy SA, Gajos KZ. Towards optimizing human-centric objectives in AI-assisted decision-making with offline reinforcement learning. arXiv:. (2024)

Huang Z, Sheng Z, Chen S. Trustworthy human-AI collaboration: reinforcement learning with human feedback and physics knowledge for safe autonomous driving. arXiv:. (2024)

Berger EJ, Guruprasad G, Senkpeil RR. Characterizing the alignment in faculty and student beliefs. In: ASEE Annual Conference and Exposition, Columbus, OH, USA, pp. 1–17 (2017)

Royce CSM, Hayes MMM, Schwartzstein RMM. Teaching critical thinking: a case for instruction in cognitive biases to reduce diagnostic errors and improve patient safety. Acad Med. 2019;94(2):187–94.

Article  Google Scholar 

Okamura K, Yamada S. AI and human-robot interaction: a review of recent advances and challenges. PLoS One. 2020;15(2):1–20.

Google Scholar 

Wang J, Lan C, Liu C, Ouyang Y, Qin T. Generalizing to unseen domains: a survey on domain generalization. IEEE Trans Knowl Data Eng. 2021;35:8052–72.

Google Scholar 

Ehrlich SK, Dean-Leon E, Tacca N, Armleder S, Dimova-Edeleva V, Cheng G. Human-robot collaborative task planning using anticipatory brain responses. PLoS One. 2023;18(7):1–20.

Article  Google Scholar 

Zoelen EM, Bosch K, Rauterberg M, Barakova E, Neerincx MA. Identifying interaction patterns of tangible co-adaptations in human-robot team behaviors. Front Psychol. 2021;12:1–16.

Google Scholar 

Okamura K, Yamada S. Adaptive trust calibration for human-AI collaboration. PloS One. 2020;15(2):1–20.

Article  Google Scholar 

Singh M, Khan SALA. Advances in autonomous robotics: integrating AI and machine learning for enhanced automation and control in industrial applications. Int J Multidimensional Res Perspect. 2024;2(4):74–90.

Article  Google Scholar 

Zanardi D, Nenna F, Orlando EM, Nannetti M, Mingardi M, Buodo G, Gamberini L. Pupil responses as indicators of learning and adaptation in human-robot collaboration scenarios. In: Proceedings of the International Conference on PErvasive Technologies Related to Assistive Environments, Crete, Greece, pp. 337–342 (2024)

Shirado H, Christakis NA. Network engineering using autonomous agents increases cooperation in human groups. iScience. 2020;23(9):1–52

Webb N, Milivojevic S, Sobhani M, Madin ZR, Ward JC, Yusuf S, Baber C, Hunt ER. Co-movement and trust development in human-robot teams. arXiv:. (2024)

Zhao F, Wood A, Mutlu B, Niedenthal P. Faces synchronize when communication through spoken language is prevented. Emotion. 2023;23(1):87–96.

Article  Google Scholar 

Nawata K, Yamaguchi H, Aoshima M. Team implicit coordination based on transactive memory systems. Team Performance Manag. 2020;26(7):375–90.

Article  Google Scholar 

Jung E, Kim I. Hybrid imitation learning framework for robotic manipulation tasks. Sensors. 2021;21(10):1–18.

Article  MathSciNet  Google Scholar 

Najar A, Bonnet E, Bahrami B, Palminteri S. The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning. PLoS Biol. 2020;18(12):1–25.

Article  Google Scholar 

Taheri A, Meghdari A, Mahoor MH. A close look at the imitation performance of children with autism and typically developing children using a robotic system. Int J Soc Robot. 2021;13(5):1125–47.

Article  Google Scholar 

Masumori A, Maruyama N, Ikegami T. Personogenesis through imitating human behavior in a humanoid robot Alter3. Front Robot AI. 2021;7:532375–88.

Article  Google Scholar 

Yeung AY, Joshi S, Williams JJ, Rudzicz F. Sequential explanations with mental model-based policies. In: Proceedings of the International Conference on Machine Learning, Virtual, pp. 1–8 (2020)

Eckstein MK, Master SL, Xia L, Dahl RE, Wilbrecht L, Collins AG. The interpretation of computational model parameters depends on the context. eLife. 2022;11:1–52

Chafii M, Naoumi S, Alami R, Almazrouei E, Bennis M, Debbah M. Emergent communication in multi-agent reinforcement learning for future wireless networks. IEEE Int Things Mag. 2023;6(4):18–24.

Article  Google Scholar 

Chen D, Zhang K, Wang Y, Yin X, Li Z, Filev D. Communication-efficient decentralized multi-agent reinforcement learning for cooperative adaptive cruise control. IEEE Trans Intell Vehic 2024;9(10):6436–49.

Wu X, Xiao L, Sun Y, Zhang J, Ma T, He L. A survey of human-in-the-loop for machine learning. Future Generation Comput Syst. 2022;135(5):364–81.

Article  Google Scholar 

Mosqueira-Rey E, Hernandez-Pereira E, Alonso-Rios D, Bobes-Bascaran J, Fernandez-Leal A. Human-in-the-loop machine learning: a state of the art. Artif Intell Rev. 2022;56:3005–54.

Article  Google Scholar 

Cranor LF. A framework for reasoning about the human in the loop. In: Conference on Usability, Psychology, and Security, San Francisco, CA, USA, pp. 1–15 (2008)

Delgado JMD, Oyedele L. Robotics in construction: a critical review of the reinforcement learning and imitation learning paradigms. Adv Eng Inf. 2022;54:101787–810.

Article  Google Scholar 

Newman BA, Paxton C, Kitani K, Admoni H. Bootstrapping linear models for fast online adaptation in human-agent collaboration. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Auckland, New Zealand, pp. 1463–1472 (2024)

Hu H, Wu DJ, Lerer A, Foerster J, Brown N. Human-AI coordination via human-regularized search and learning. arXiv:. (2022)

Arora S, Doshi P. A survey of inverse reinforcement learning: challenges, methods and progress. Artif Intell. 2021;297:103500–1003527.

Article  MathSciNet  Google Scholar 

Myers V, Ellis E, Levine S, Eysenbach B, Dragan A. Learning to assist humans without inferring rewards. In: Advances in Neural Information Processing Systems, Vancouver, BC, Canada, pp. 1–13 (2024)

Jacob AP, Wu DJ, Farina G, Lerer A, Hu H, Bakhtin A, Andreas J, Brown N. Modeling strong and human-like gameplay with KL-regularized search. In: Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA, pp. 9695–9728 (2022)

Erven T, Harremos P. Renyi divergence and Kullback-Leibler divergence. IEEE Trans Inf Theory. 2014;60(7):3797–820.

Article  Google Scholar 

Barrett S, Rosenfeld A, Kraus S, Stone P. Making friends on the fly: cooperating with new teammates. Artif Intell. 2017;242:132–71.

Article  MathSciNet  Google Scholar 

Lupu A, Cui B, Hu H, Foerster J. Trajectory diversity for zero-shot coordination. In: Proceedings of the International Conference on Machine Learning, Virtual, pp. 7204–7213 (2021)

Bhattacharyya R, Wulfe B, Phillips DJ, Kuefler A, Morton J, Senanayake R, et al. Modeling human driving behavior through generative adversarial imitation learning. IEEE Trans Intell Transport Syst. 2023;24(3):2874–87.

Article  Google Scholar 

Tucker M, Zhou Y, Shah J. Adversarially guided self-play for adopting social conventions. arXiv:. (2020)

Zhang R, Xu Z, Ma C, Yu C, Tu W, Tang W, Huang S, Ye D, Ding W, Yang Y, Wang Y. A survey on self-play methods in reinforcement learning. arXiv:. (2025)

Lucas K, Allen RE. Any-play: an intrinsic augmentation for zero-shot coordination. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Virtual, pp. 853–861 (2022)

Dennis M, Jaques N, Vinitsky E, Bayen A, Russell S, Critch A, Levine S. Emergent complexity and zero-shot transfer via unsupervised environment design. In: Advances in Neural Information Processing Systems, Virtual, pp. 13049–13061 (2020)

Liang A, Czempin P, Zhou Y, Tu S, Biyik E. In-context generalization to new tasks from unlabeled observation data. In: Proceedings of the International Conference on Machine Learning, Vienna, Austria, pp. 1–10 (2024)

Grover A, Al-Shedivat M, Gupta J, Burda Y, Edwards H. Learning policy representations in multiagent systems. In: Proceedings of the International Conference on Machine Learning, Stockholm, Sweden, pp. 1802–1811 (2018)

He JZ-Y, Erickson Z, Brown DS, Raghunathan A, Dragan A. Learning representations that enable generalization in assistive tasks. In: Proceedings of the Conference on Robot Learning, Atlanta, GA, USA, pp. 2105–2114 (2023)

Pinto L, Davidson J, Sukthankar R, Gupta A. Robust adversarial reinforcement learning. In: Proceedings of the International Conference on Machine Learning, Sydney, Australia, pp. 2817–2826 (2017)

Leslie AM, Friedman O, German TP. Core mechanisms in ‘theory of mind’. Trends Cognitive Sci. 2004;8(12):528–33.

Article  Google Scholar 

Wellman HM. Theory of mind: the state of the art. Eur J Develop Psychol. 2018;15(6):728–55.

Article  Google Scholar 

Chen S, Andrejczuk E, Cao Z, Zhang J. AATEAM: achieving the ad hoc teamwork by employing the attention mechanism. In: Proceedings of the AAAI Conference on Artificial Intelligence, New York City, NY, USA, pp. 7095–7102 (2020)

Mirsky R, Carlucho I, Rahman A, Fosong E, Macke W, Sridharan M, Stone P, Albrecht SV. A survey of ad hoc teamwork research. In: European Conference on Multi-Agent Systems, Bucharest, Romania, pp. 275–293 (2022)

Bansal S, Xu J, Morales M, Streater J, Howard A Jr C. Cognitive bias for human-AI ad hoc teamwork. In: Advances in Neural Information Processing Systems, Vancouver, BC, Canada, pp. 1–6 (2024)

Sarkar B, Shih A, Sadigh D. Diverse conventions for human-AI collaboration. In: Advances in Neural Information Processing Systems, New Orleans, LA, USA, pp. 23115–23139 (2023)

Raileanu R, Denton E, Szlam A, Fergus R. Modeling others using oneself in multi-agent reinforcement learning. In: Proceedings of the International Conference on Machine Learning, Stockholm, Sweden, pp. 4257–4266 (2018)

Nguyen D, Le H, Do K, Gupta S, Venkatesh S, Tran T. Diversifying training pool predictability for zero-shot coordination: a theory of mind approach. In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, Jeju Island, South Korea, pp. 166–174 (2024)

Muglich D, Zintgraf LM, Witt CASD, Whiteson S, Foerster J. Generalized beliefs for cooperative AI. In: Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA, pp. 16062–16082 (2022)

Yu C, Gao J, Liu W, Xu B, Tang H, Yang J, Wang Y, Wu Y. Learning zero-shot cooperation with humans, assuming humans are biased. In: The International Conference on Learning Representations, Kigali, Rwanda, pp. 1–31 (2023)

Xie A, Losey D, Tolsma R, Finn C, Sadigh D. Learning latent representations to influence multi-agent interaction. In: Proceedings of the Conference on Robot Learning, Virtual, pp. 575–588 (2021)

Liang Y, Chen D, Gupta A, Du SS, Jaques N. Learning to cooperate with humans using generative agents. In: Advances in Neural Information Processing Systems, Vancouver, BC, Canada, pp. 1–21 (2024)

Li X, Zhang T, Liu C, Meng L, Xu B. Long short-term reasoning network with theory of mind for efficient multi-agent cooperation. In: International Joint Conference on Neural Networks, Yokohama, Japan, pp. 1–8 (2024)

Yu G, Kasumba R, Ho C-J, Yeoh W. On the utility of accounting for human beliefs about AI intention in human-AI collaboration. arXiv:. (2024)

Wang RE, Wu SA, Evans JA, Tenenbaum JB, Parkes DC, Kleiman-Weiner M. Too many cooks: coordinating multi-agent collaboration through inverse planning. In: Proceedings of the International Conference on Autonomous Agents and MultiAgent Systems, Auckland, New Zealand, pp. 2032–2034 (2020)

Wang C, Chen Z, Liu H. On the utility of external agent intention predictor for human-AI coordination. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Auckland, New Zealand, pp. 2546–2548 (2024)

Hu H, Foerster JN. Simplified action decoder for deep multi-agent reinforcement learning. arXiv:. (2021)

Petersen SE, Sporns O. Brain networks and cognitive architectures. Neuron. 2015;88(1):207–19.

Article  Google Scholar 

Yan X, Guo J, Lou X, Wang J, Zhang H, Du Y. An efficient end-to-end training approach for zero-shot human-AI coordination. In: Advances in Neural Information Processing Systems, New Orleans, LA, USA, pp. 2636–2658 (2023)

Su E, Raffe W, Mathieson L, Wang Y. Better understanding of humans for cooperative AI through clustering. In: IEEE Conference on Games, Milan, Italy, pp. 1–8 (2024)

Gao Y, Liu F, Wang L, Zheng D, Lian Z, Wang W, Yang W, Li S, Wang X, Chen W, Dai J, FU Q, Wei Y, Huang L, Liu W. Enhancing human experience in human-agent collaboration: a human-centered modeling approach based on positive human gain. In: The International Conference on Learning Representations, Vienna, Austria, pp. 1–29 (2024)

Kazantzidis I, Norman T, Du Y, Freeman C. How to train your agent: active learning from human preferences and justifications in safety-critical environments. In: Proceedings of the International C