{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:30:03Z","timestamp":1743035403273,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031159077"},{"type":"electronic","value":"9783031159084"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-15908-4_21","type":"book-chapter","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T13:04:24Z","timestamp":1662123864000},"page":"266-281","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A-EMS: An Adaptive Emergency Management System for\u00a0Autonomous Agents in\u00a0Unforeseen Situations"],"prefix":"10.1007","author":[{"given":"Glenn","family":"Maguire","sequence":"first","affiliation":[]},{"given":"Nicholas","family":"Ketz","sequence":"additional","affiliation":[]},{"given":"Praveen K.","family":"Pilly","sequence":"additional","affiliation":[]},{"given":"Jean-Baptiste","family":"Mouret","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"key":"21_CR1","unstructured":"Achiam, J., Amodei, D.: Benchmarking safe exploration in deep reinforcement learning. In: NeurIPS Deep Reinforcement Learning Workshop (2019). https:\/\/d4mucfpksywv.cloudfront.net\/safexp-short.pdf"},{"key":"21_CR2","unstructured":"Achiam, J., Held, D., Tamar, A., Abbeel, P.: Constrained policy optimization. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 22\u201331. JMLR.org, Cambridge (2017)"},{"issue":"6","key":"21_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","volume":"34","author":"K Arulkumaran","year":"2017","unstructured":"Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.: Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34(6), 26\u201338 (2017)","journal-title":"IEEE Signal Process. Mag."},{"key":"21_CR4","unstructured":"Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 1, pp. 1171\u20131179. MIT Press, Cambridge (2015)"},{"key":"21_CR5","unstructured":"Brochu, E., Cora, M., de Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Technical report TR-2009-023, Department of Computer Science, University of British Columbia (2010)"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Brown, N., Sandholm, T.: Libratus: the superhuman AI for no-limit poker. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 5226\u20135228. IJCAI Organization, Menlo Park (2017)","DOI":"10.24963\/ijcai.2017\/772"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Caselles-Dupr\u00e9, H., Garcia-Ortiz, M., Filliat, D.: S-TRIGGER: continual state representation learning via self-triggered generative replay. In: International Joint Conference on Neural Networks (IJCNN 2021) (2021, accepted)","DOI":"10.1109\/IJCNN52387.2021.9533683"},{"issue":"3","key":"21_CR8","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/TNNLS.2016.2522401","volume":"28","author":"Y Deng","year":"2017","unstructured":"Deng, Y., Bao, F., Kong, Y., Ren, Z., Dai, Q.: Deep direct reinforcement learning for financial signal representation and trading. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 653\u2013664 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"21_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. 3521\u20133526. PMLR, Bletchley Park (2017)"},{"issue":"7","key":"21_CR10","doi-asserted-by":"publisher","first-page":"2737","DOI":"10.1109\/TAC.2018.2876389","volume":"64","author":"JF Fisac","year":"2019","unstructured":"Fisac, J.F., Akametalu, A.K., Zeilinger, M.N., Kaynama, S., Gillula, J., Tomlin, C.J.: A general safety framework for learning-based control in uncertain robotic systems. IEEE Trans. Autom. Control 64(7), 2737\u20132752 (2019)","journal-title":"IEEE Trans. Autom. Control"},{"issue":"3\u20134","key":"21_CR11","first-page":"219","volume":"11","author":"V Francois-Lavet","year":"2018","unstructured":"Francois-Lavet, V., Henderson, P., Islam, R., Bellemare, M., Pineau, J.: An introduction to deep reinforcement learning. IEEE Signal Process. Mag. 11(3\u20134), 219\u2013354 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"21_CR12","unstructured":"Ha, D., Schmidhuber, J.: World models. arXiv:1803.10122v4 [cs.LG] (2018)"},{"key":"21_CR13","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv:1312.6114v10 [stat.ML] (2014)"},{"issue":"1","key":"21_CR14","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/BF01589116","volume":"45","author":"D Liu","year":"1989","unstructured":"Liu, D., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45(1), 503\u2013528 (1989)","journal-title":"Math. Program."},{"issue":"7","key":"21_CR15","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1016\/j.neucom.2006.05.013","volume":"70","author":"L Manevitz","year":"2007","unstructured":"Manevitz, L., Yousef, M.: One-class document classification via neural networks. Neurocomputing 70(7), 1466\u20131481 (2007)","journal-title":"Neurocomputing"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Marchi, E., Vesperini, F., Eyben, F., Squartini, S., Schuller, B.: A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1996\u20132000. IEEE Press, Piscataway (2015)","DOI":"10.1109\/ICASSP.2015.7178320"},{"issue":"7540","key":"21_CR17","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"volume-title":"Bayesian Approach to Global Optimization: Theory and Applications","year":"2013","key":"21_CR18","unstructured":"Mockus, J. (ed.): Bayesian Approach to Global Optimization: Theory and Applications. Kluwer Academic Publishers, Boston (2013)"},{"key":"21_CR19","unstructured":"Nguyen, C.V., Li, Y., Bui, T.D., Turner, R.E.: Variational continual learning. In: Sixth International Conference on Learning Representations, pp. 1\u201318. iclr.cc, La Jolla (2018)"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Pan, X., You, Y., Wang, Z., Lu, C.: Virtual to real reinforcement learning for autonomous driving. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 11.1\u201311.13. BMVA Press, London (2017)","DOI":"10.5244\/C.31.11"},{"issue":"4","key":"21_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073602","volume":"36","author":"XB Peng","year":"2017","unstructured":"Peng, X.B., Berseth, G., Yin, K., Van De Panne, M.: DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning. ACM Trans. Graph. 36(4), 1\u201313 (2017)","journal-title":"ACM Trans. Graph."},{"volume-title":"Gaussian Processes for Machine Learning","year":"2006","key":"21_CR22","unstructured":"Rasmussen, C.E., Williams, C.K.I. (eds.): Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Richter, C., Roy, N.: Safe visual navigation via deep learning and novelty detection. In: Proceedings of Robotics: Science and Systems, pp. 1\u20139. MIT Press, Cambridge (2017)","DOI":"10.15607\/RSS.2017.XIII.064"},{"key":"21_CR24","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: 2nd International Conference on Learning Representations, ICLR 2014, pp. 1\u201310. iclr.cc, La Jolla (2014)"},{"key":"21_CR25","unstructured":"Tessler, C., Mankowitz, D.J., Mannor, S.: Reward constrained policy optimization. arXiv:1805.11074v3 [cs.LG] (2018)"},{"issue":"3","key":"21_CR26","doi-asserted-by":"publisher","first-page":"4915","DOI":"10.1109\/LRA.2021.3070252","volume":"6","author":"B Thananjeyan","year":"2021","unstructured":"Thananjeyan, B., et al.: Recovery RL: safe reinforcement learning with learned recovery zones. IEEE Rob. Autom. Lett. 6(3), 4915\u20134922 (2021)","journal-title":"IEEE Rob. Autom. Lett."},{"issue":"2","key":"21_CR27","doi-asserted-by":"publisher","first-page":"3612","DOI":"10.1109\/LRA.2020.2976272","volume":"5","author":"B Thananjeyan","year":"2020","unstructured":"Thananjeyan, B., et al.: Safety augmented value estimation from demonstrations (SAVED): safe deep model-based RL for sparse cost robotic tasks. IEEE Rob. Autom. Lett. 5(2), 3612\u20133619 (2020)","journal-title":"IEEE Rob. Autom. Lett."}],"container-title":["Lecture Notes in Computer Science","Towards Autonomous Robotic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15908-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T13:07:10Z","timestamp":1662124030000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15908-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031159077","9783031159084"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15908-4_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TAROS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference Towards Autonomous Robotic Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Culham","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"taros2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ukaeaevents.com\/23rd-taros\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38","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":"14","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":"10","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":"37% - 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":"2.5","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)"}}]}}