{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:17:00Z","timestamp":1766067420864,"version":"3.44.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030864859"},{"type":"electronic","value":"9783030864866"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86486-6_9","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T15:25:48Z","timestamp":1631201148000},"page":"139-156","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-agent Imitation Learning with Copulas"],"prefix":"10.1007","author":[{"given":"Hongwei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Lantao","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Zhangjie","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Stefano","family":"Ermon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social lstm: Human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961\u2013971 (2016)","DOI":"10.1109\/CVPR.2016.110"},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.artint.2018.01.002","volume":"258","author":"SV Albrecht","year":"2018","unstructured":"Albrecht, S.V., Stone, P.: Autonomous agents modelling other agents: a comprehensive survey and open problems. Artif. Intell. 258, 66\u201395 (2018)","journal-title":"Artif. Intell."},{"key":"9_CR3","unstructured":"Battaglia, P., Pascanu, R., Lai, M., Rezende, D.J., et al.: Interaction networks for learning about objects, relations and physics. In: Advances in Neural Information Processing Systems, pp. 4502\u20134510 (2016)"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, R.P., Phillips, D.J., Wulfe, B., Morton, J., Kuefler, A., Kochenderfer, M.J.: Multi-agent imitation learning for driving simulation. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1534\u20131539. IEEE (2018)","DOI":"10.1109\/IROS.2018.8593758"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Bouy\u00e9, E., Durrleman, V., Nikeghbali, A., Riboulet, G., Roncalli, T.: Copulas for finance-a reading guide and some applications. SSRN 1032533 (2000)","DOI":"10.2139\/ssrn.1032533"},{"issue":"6456","key":"9_CR6","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1126\/science.aay2400","volume":"365","author":"N Brown","year":"2019","unstructured":"Brown, N., Sandholm, T.: Superhuman AI for multiplayer poker. Science 365(6456), 885\u2013890 (2019)","journal-title":"Science"},{"issue":"3","key":"9_CR7","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1109\/TITS.2019.2901791","volume":"21","author":"T Chu","year":"2019","unstructured":"Chu, T., Wang, J., Codec\u00e0, L., Li, Z.: Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Trans. Intell. Transp. Syst. 21(3), 1086\u20131095 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"9_CR8","series-title":"Selected Works in Probability and Statistics","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-1-4419-8339-8_13","volume-title":"Selected Works of Murray Rosenblatt","author":"RA Davis","year":"2011","unstructured":"Davis, R.A., Lii, K.-S., Politis, D.N.: Remarks on some nonparametric estimates of a density function. In: Selected Works of Murray Rosenblatt. SWPS, pp. 95\u2013100. Springer, New York (2011). https:\/\/doi.org\/10.1007\/978-1-4419-8339-8_13"},{"key":"9_CR9","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1\u201316 (2017)"},{"key":"9_CR10","unstructured":"Fu, J., Luo, K., Levine, S.: Learning robust rewards with adversarial inverse reinforcement learning. arXiv preprint arXiv:1710.11248 (2017)"},{"key":"9_CR11","unstructured":"Ho, J., Ermon, S.: Generative adversarial imitation learning. In: Advances in Neural Information Processing Systems, pp. 4565\u20134573 (2016)"},{"key":"9_CR12","unstructured":"Hoshen, Y.: Vain: attentional multi-agent predictive modeling. In: Advances in Neural Information Processing Systems, pp. 2701\u20132711 (2017)"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Ivanovic, B., Schmerling, E., Leung, K., Pavone, M.: Generative modeling of multimodal multi-human behavior. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3088\u20133095. IEEE (2018)","DOI":"10.1109\/IROS.2018.8594393"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Joe, H.: Dependence modeling with copulas. Chapman and Hall\/CRC (2014)","DOI":"10.1201\/b17116"},{"key":"9_CR15","unstructured":"Kipf, T.N., Fetaya, E., Wang, K.-C., Welling, M., Zemel, R.S.: Neural relational inference for interacting systems. In: International Conference on Machine Learning (2018)"},{"key":"9_CR16","unstructured":"Le, H.M., Yue, Y., Carr, P., Lucey, P.: Coordinated multi-agent imitation learning. In: Proceedings of the 34th International Conference on Machine Learning, pp. 1995\u20132003 (2017)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Li, M.G., Jiang, B., Zhu, H., Che, Z., Liu, Y.: Generative attention networks for multi-agent behavioral modeling. In: AAAI, pp. 7195\u20137202 (2020)","DOI":"10.1609\/aaai.v34i05.6209"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Machine learning proceedings 1994, pp. 157\u2013163. Elsevier (1994)","DOI":"10.1016\/B978-1-55860-335-6.50027-1"},{"key":"9_CR19","unstructured":"Liu, M.: Multi-agent interactions modeling with correlated policies. arXiv preprint arXiv:2001.03415 (2020)"},{"key":"9_CR20","unstructured":"Michael, O., Obst, O., Schmidsberger, F., Stolzenburg, F.: Robocupsimdata: a robocup soccer research dataset. arXiv preprint arXiv:1711.01703 (2017)"},{"key":"9_CR21","unstructured":"Nelsen, R.B.: An Introduction to Copulas. Springer Science & Business Media (2007)"},{"key":"9_CR22","unstructured":"Ng, A.Y., Russell, S.J., et al.: Algorithms for inverse reinforcement learning. In: ICML, vol. 1, p. 2 (2000)"},{"issue":"3","key":"9_CR23","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","volume":"33","author":"E Parzen","year":"1962","unstructured":"Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065\u20131076 (1962)","journal-title":"Ann. Math. Stat."},{"issue":"1","key":"9_CR24","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1162\/neco.1991.3.1.88","volume":"3","author":"DA Pomerleau","year":"1991","unstructured":"Pomerleau, D.A.: Efficient training of artificial neural networks for autonomous navigation. Neural Comput. 3(1), 88\u201397 (1991)","journal-title":"Neural Comput."},{"key":"9_CR25","volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","author":"ML Puterman","year":"2014","unstructured":"Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (2014)"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Russell, S.: Learning agents for uncertain environments. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 101\u2013103 (1998)","DOI":"10.1145\/279943.279964"},{"issue":"7676","key":"9_CR27","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"550","author":"D Silver","year":"2017","unstructured":"Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354\u2013359 (2017)","journal-title":"Nature"},{"key":"9_CR28","first-page":"229","volume":"8","author":"A Sklar","year":"1959","unstructured":"Sklar, A.: Fonctions de R\u00e9partition \u00e0 n dimensions et leurs marges. Publications de L\u2019Institut de Statistique de L\u2019Universit\u00e9 de Paris 8, 229\u2013231 (1959)","journal-title":"Publications de L\u2019Institut de Statistique de L\u2019Universit\u00e9 de Paris"},{"key":"9_CR29","first-page":"229","volume":"8","author":"M Sklar","year":"1959","unstructured":"Sklar, M.: Fonctions de repartition an dimensions et leurs marges. Publ. inst. statist. univ. Paris 8, 229\u2013231 (1959)","journal-title":"Publ. inst. statist. univ. Paris"},{"key":"9_CR30","unstructured":"Song, J., Ren, H., Sadigh, D., Ermon, S.: Multi-agent generative adversarial imitation learning. In: Advances in Neural Information Processing Systems, pp. 7461\u20137472 (2018)"},{"key":"9_CR31","unstructured":"Sukhbaatar, S., Fergus, R., et al.: Learning multiagent communication with backpropagation. In: Advances in Neural Information Processing Systems, pp. 2244\u20132252 (2016)"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Syed, U., Bowling, M., Schapire, R.E.: Apprenticeship learning using linear programming. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1032\u20131039 (2008)","DOI":"10.1145\/1390156.1390286"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Tian, Z., Wen, Y., Gong, Z., Punakkath, F., Zou, S., Wang, J.: A regularized opponent model with maximum entropy objective. arXiv preprint arXiv:1905.08087 (2019)","DOI":"10.24963\/ijcai.2019\/85"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Yeh, R.A., Schwing, A.G., Huang, J., Murphy, K.: Diverse generation for multi-agent sports games. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4610\u20134619 (2019)","DOI":"10.1109\/CVPR.2019.00474"},{"key":"9_CR35","unstructured":"Yu, L., Song, J., Ermon, S.: Multi-agent adversarial inverse reinforcement learning. In: International Conference on Machine Learning (2019)"},{"key":"9_CR36","unstructured":"Zhan, E., Zheng, S., Yue, Y., Sha, L., Lucey, P.: Generating multi-agent trajectories using programmatic weak supervision. arXiv preprint arXiv:1803.07612 (2018)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86486-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:06:49Z","timestamp":1757369209000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86486-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030864859","9783030864866"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86486-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"210","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}