{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:42:41Z","timestamp":1784738561977,"version":"3.55.0"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031510250","type":"print"},{"value":"9783031510267","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-51026-7_16","type":"book-chapter","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T14:02:08Z","timestamp":1705759328000},"page":"179-188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Experience Sharing and\u00a0Human-in-the-Loop Optimization for\u00a0Federated Robot Navigation Recommendation"],"prefix":"10.1007","author":[{"given":"Morteza","family":"Moradi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Moradi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario Calogero","family":"Guastella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,21]]},"reference":[{"issue":"7729","key":"16_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/s41586-018-0637-6","volume":"563","author":"E Awad","year":"2018","unstructured":"Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.F., Rahwan, I.: The moral machine experiment. Nature 563(7729), 59\u201364 (2018)","journal-title":"Nature"},{"key":"16_CR2","first-page":"374","volume":"1","author":"K Bonawitz","year":"2019","unstructured":"Bonawitz, K., et al.: Towards federated learning at scale: System design. Proceedings of Machine Learning and Systems 1, 374\u2013388 (2019)","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Cavorsi, M., Akg\u00fcn, O.E., Yemini, M., Goldsmith, A.J., Gil, S.: Exploiting trust for resilient hypothesis testing with malicious robots. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 7663\u20137669. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10160385"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1613\/jair.1.11332","volume":"67","author":"A Checco","year":"2020","unstructured":"Checco, A., Bates, J., Demartini, G.: Adversarial attacks on crowdsourcing quality control. J. Artif. Intell. Res. 67, 375\u2013408 (2020)","journal-title":"J. Artif. Intell. Res."},{"key":"16_CR5","unstructured":"Chernova, S., Orkin, J., Breazeal, C.: Crowdsourcing HRI through online multiplayer games. In: Dialog with Robots, Papers from the 2010 AAAI Fall Symposium, Arlington, Virginia, USA, November 11\u201313, 2010. AAAI Technical Report, vol. FS-10-05. AAAI (2010). https:\/\/www.aaai.org\/ocs\/index.php\/FSS\/FSS10\/paper\/view\/2212"},{"key":"16_CR6","first-page":"10707","volume":"33","author":"F Christianos","year":"2020","unstructured":"Christianos, F., Sch\u00e4fer, L., Albrecht, S.: Shared experience actor-critic for multi-agent reinforcement learning. Adv. Neural. Inf. Process. Syst. 33, 10707\u201310717 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"6","key":"16_CR7","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/MCS.2020.3019725","volume":"40","author":"AP Dani","year":"2020","unstructured":"Dani, A.P., Salehi, I., Rotithor, G., Trombetta, D., Ravichandar, H.: Human-in-the-loop robot control for human-robot collaboration: human intention estimation and safe trajectory tracking control for collaborative tasks. IEEE Control Syst. Mag. 40(6), 29\u201356 (2020)","journal-title":"IEEE Control Syst. Mag."},{"issue":"1","key":"16_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3148148","volume":"51","author":"F Daniel","year":"2018","unstructured":"Daniel, F., Kucherbaev, P., Cappiello, C., Benatallah, B., Allahbakhsh, M.: Quality control in crowdsourcing: a survey of quality attributes, assessment techniques, and assurance actions. ACM Comput. Surv. (CSUR) 51(1), 1\u201340 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"16_CR9","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s12599-019-00595-2","volume":"61","author":"D Dellermann","year":"2019","unstructured":"Dellermann, D., Ebel, P., S\u00f6llner, M., Leimeister, J.M.: Hybrid intelligence. Bus. Inf. Syst. Eng. 61, 637\u2013643 (2019)","journal-title":"Bus. Inf. Syst. Eng."},{"issue":"16","key":"16_CR10","doi-asserted-by":"publisher","first-page":"14185","DOI":"10.1109\/JIOT.2020.3018878","volume":"9","author":"W Feng","year":"2020","unstructured":"Feng, W., Yan, Z., Yang, L.T., Zheng, Q.: Anonymous authentication on trust in blockchain-based mobile crowdsourcing. IEEE Internet Things J. 9(16), 14185\u201314202 (2020)","journal-title":"IEEE Internet Things J."},{"key":"16_CR11","unstructured":"Foerster, J., et al.: Stabilising experience replay for deep multi-agent reinforcement learning. In: International Conference on Machine Learning, pp. 1146\u20131155. PMLR (2017)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Forbes, M., Chung, M., Cakmak, M., Rao, R.: Robot programming by demonstration with crowdsourced action fixes. In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, vol. 2, pp. 67\u201376 (2014)","DOI":"10.1609\/hcomp.v2i1.13164"},{"key":"16_CR13","unstructured":"Fridman, L., Terwilliger, J., Jenik, B.: Deeptraffic: Crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi-agent dense traffic navigation. arXiv preprint arXiv:1801.02805 (2018)"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Gadiraju, U., Kawase, R., Dietze, S., Demartini, G.: Understanding malicious behavior in crowdsourcing platforms: The case of online surveys. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1631\u20131640 (2015)","DOI":"10.1145\/2702123.2702443"},{"key":"16_CR15","doi-asserted-by":"publisher","unstructured":"Grace, K., Salvatier, J., Dafoe, A., Zhang, B., Evans, O.: Viewpoint: When will AI exceed human performance? evidence from AI experts. J. Artif. Intell. Res. 62, 729\u2013754 (2018). https:\/\/doi.org\/10.1613\/jair.1.11222. https:\/\/doi.org\/10.1613\/jair.1.11222","DOI":"10.1613\/jair.1.11222"},{"key":"16_CR16","unstructured":"Halmes, M.: Measurements of collective machine intelligence. CoRR abs\/1306.6649 (2013). https:\/\/arxiv.org\/abs\/1306.6649"},{"issue":"4","key":"16_CR17","doi-asserted-by":"publisher","first-page":"6569","DOI":"10.1109\/LRA.2021.3093551","volume":"6","author":"H Hu","year":"2021","unstructured":"Hu, H., Zhang, K., Tan, A.H., Ruan, M., Agia, C., Nejat, G.: A sim-to-real pipeline for deep reinforcement learning for autonomous robot navigation in cluttered rough terrain. IEEE Robot. Autom. Lett. 6(4), 6569\u20136576 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"5","key":"16_CR18","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1109\/TKDE.2019.2896926","volume":"32","author":"Q Hu","year":"2019","unstructured":"Hu, Q., Wang, S., Ma, P., Cheng, X., Lv, W., Bie, R.: Quality control in crowdsourcing using sequential zero-determinant strategies. IEEE Trans. Knowl. Data Eng. 32(5), 998\u20131009 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Imteaj, A., Amini, M.H.: Fedar: activity and resource-aware federated learning model for distributed mobile robots. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1153\u20131160. IEEE (2020)","DOI":"10.1109\/ICMLA51294.2020.00185"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Jain, A., Das, D., Gupta, J.K., Saxena, A.: Planit: a crowdsourcing approach for learning to plan paths from large scale preference feedback. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 877\u2013884. IEEE (2015)","DOI":"10.1109\/ICRA.2015.7139281"},{"issue":"4","key":"16_CR21","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1109\/JAS.2019.1911732","volume":"7","author":"L Jiang","year":"2019","unstructured":"Jiang, L., Huang, H., Ding, Z.: Path planning for intelligent robots based on deep q-learning with experience replay and heuristic knowledge. IEEE\/CAA J. Automatica Sinica 7(4), 1179\u20131189 (2019)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"issue":"1869","key":"16_CR22","doi-asserted-by":"publisher","first-page":"20210447","DOI":"10.1098\/rstb.2021.0447","volume":"378","author":"S Levine","year":"2023","unstructured":"Levine, S., Shah, D.: Learning robotic navigation from experience: principles, methods and recent results. Philos. Trans. R. Soc. B 378(1869), 20210447 (2023)","journal-title":"Philos. Trans. R. Soc. B"},{"key":"16_CR23","doi-asserted-by":"publisher","unstructured":"Li, L., Bayuelo, A., Bobadilla, L., Alam, T., Shell, D.A.: Coordinated multi-robot planning while preserving individual privacy. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 2188\u20132194 (2019). https:\/\/doi.org\/10.1109\/ICRA.2019.8794460","DOI":"10.1109\/ICRA.2019.8794460"},{"key":"16_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.110555","volume":"246","author":"C Liu","year":"2022","unstructured":"Liu, C., et al.: Human-machine cooperation research for navigation of maritime autonomous surface ships: a review and consideration. Ocean Eng. 246, 110555 (2022)","journal-title":"Ocean Eng."},{"key":"16_CR25","unstructured":"O\u2019Mahony, M.P., Hurley, N.J., Silvestre, G.C.: Recommender systems: attack types and strategies. In: AAAI, pp. 334\u2013339 (2005)"},{"key":"16_CR26","doi-asserted-by":"publisher","unstructured":"Qiao, N., Sun, Y., Liu, C., Xia, L., Luo, J., Zhang, K., Kuo, C.: Human-in-the-loop video semantic segmentation auto-annotation. In: IEEE\/CVF Winter Conference on Applications of Computer Vision, WACV 2023, Waikoloa, HI, USA, January 2\u20137, 2023, pp. 5870\u20135880. IEEE (2023). https:\/\/doi.org\/10.1109\/WACV56688.2023.00583","DOI":"10.1109\/WACV56688.2023.00583"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Qiu, C., Squicciarini, A.C., Carminati, B., Caverlee, J., Khare, D.R.: Crowdselect: increasing accuracy of crowdsourcing tasks through behavior prediction and user selection. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 539\u2013548 (2016)","DOI":"10.1145\/2983323.2983830"},{"key":"16_CR28","doi-asserted-by":"publisher","unstructured":"Reddy, S., Dragan, A.D., Levine, S.: Shared autonomy via deep reinforcement learning. In: Kress-Gazit, H., Srinivasa, S.S., Howard, T., Atanasov, N. (eds.) Robotics: Science and Systems XIV, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA, June 26\u201330, 2018 (2018). https:\/\/doi.org\/10.15607\/RSS.2018.XIV.005. https:\/\/www.roboticsproceedings.org\/rss14\/p05.html","DOI":"10.15607\/RSS.2018.XIV.005"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Shilov, N.: Recommender system for navigation safety: Requirements and methodology. TransNav: Int. J. Marine Navigation Saf. Sea Transp. 14(2) (2020)","DOI":"10.12716\/1001.14.02.18"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354\u2013359 (2017)","DOI":"10.1038\/nature24270"},{"key":"16_CR31","doi-asserted-by":"publisher","first-page":"128101","DOI":"10.1109\/ACCESS.2022.3227076","volume":"10","author":"TK Tasooji","year":"2022","unstructured":"Tasooji, T.K., Marquez, H.J.: A secure decentralized event-triggered cooperative localization in multi-robot systems under cyber attack. IEEE Access 10, 128101\u2013128121 (2022)","journal-title":"IEEE Access"},{"key":"16_CR32","unstructured":"Wang, G., Wang, T., Zheng, H., Zhao, B.Y.: Man vs. machine: practical adversarial detection of malicious crowdsourcing workers. In: 23rd USENIX Security Symposium (USENIX Security 14), pp. 239\u2013254 (2014)"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Yaacoub, J.P.A., Noura, H.N., Salman, O., Chehab, A.: Robotics cyber security: Vulnerabilities, attacks, countermeasures, and recommendations. Int. J. Inf. Secur., 1\u201344 (2022)","DOI":"10.1007\/s10207-021-00545-8"},{"key":"16_CR34","doi-asserted-by":"publisher","first-page":"209191","DOI":"10.1109\/ACCESS.2020.3038287","volume":"8","author":"Y Ye","year":"2020","unstructured":"Ye, Y., Li, S., Liu, F., Tang, Y., Hu, W.: Edgefed: optimized federated learning based on edge computing. IEEE Access 8, 209191\u2013209198 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3038287","journal-title":"IEEE Access"},{"key":"16_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2022.110182","volume":"139","author":"K Zhang","year":"2022","unstructured":"Zhang, K., Li, Z., Wang, Y., Louati, A., Chen, J.: Privacy-preserving dynamic average consensus via state decomposition: case study on multi-robot formation control. Automatica 139, 110182 (2022). https:\/\/doi.org\/10.1016\/j.automatica.2022.110182","journal-title":"Automatica"},{"key":"16_CR36","doi-asserted-by":"publisher","unstructured":"Zhang, R., Torabi, F., Guan, L., Ballard, D.H., Stone, P.: Leveraging human guidance for deep reinforcement learning tasks. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10\u201316, 2019. pp. 6339\u20136346. ijcai.org (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/884. https:\/\/doi.org\/10.24963\/ijcai.2019\/884","DOI":"10.24963\/ijcai.2019\/884"},{"key":"16_CR37","doi-asserted-by":"publisher","unstructured":"Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 5:1\u20135:38 (2019). https:\/\/doi.org\/10.1145\/3285029. https:\/\/doi.org\/10.1145\/3285029","DOI":"10.1145\/3285029"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing - ICIAP 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-51026-7_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T05:13:00Z","timestamp":1711343580000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-51026-7_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031510250","9783031510267"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-51026-7_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Udine","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2023.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"144","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":"82","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":"13","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":"57% - 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","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","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":"https:\/\/iciap2023.org\/satellite-event\/workshops\/","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)"}}]}}