{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:52:07Z","timestamp":1742914327743,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031504846"},{"type":"electronic","value":"9783031504853"}],"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\/978-3-031-50485-3_34","type":"book-chapter","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T06:02:28Z","timestamp":1706076148000},"page":"332-345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Process Steps as\u00a0Dynamical Systems for\u00a0a\u00a0Sub-Symbolic Approach of\u00a0Process Planning in\u00a0Cyber-Physical Production Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5023-839X","authenticated-orcid":false,"given":"Jonas","family":"Ehrhardt","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1147-8205","authenticated-orcid":false,"given":"Ren\u00e9","family":"Heesch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8747-3596","authenticated-orcid":false,"given":"Oliver","family":"Niggemann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"issue":"10","key":"34_CR1","first-page":"11937","volume":"37","author":"L Amado","year":"2023","unstructured":"Amado, L., Pereira, R.F., Meneguzzi, F.: Robust neuro-symbolic goal and plan recognition. Proc. AAAI Conf. Artif. Intell. 37(10), 11937\u201311944 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"34_CR2","unstructured":"Ardizzone, L., Kruse, J., Rother, C., K\u00f6the, U.: Analyzing inverse problems with invertible neural networks. In: International Conference on Learning Representations (2019)"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Asai, M., Muise, C.: Learning neural-symbolic descriptive planning models via cube-space priors: the voyage home (to strips) (2020)","DOI":"10.24963\/ijcai.2020\/371"},{"issue":"1","key":"34_CR4","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1109\/TII.2022.3146940","volume":"19","author":"K Balzereit","year":"2023","unstructured":"Balzereit, K., Niggemann, O.: Autoconf a new algorithm for reconfiguration of cyber-physical production systems. IEEE Trans. Industr. Inf. 19(1), 739\u2013749 (2023)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"34_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1007\/978-3-030-22999-3_58","volume-title":"Advances and Trends in Artificial Intelligence. From Theory to Practice","author":"A Bit-Monnot","year":"2019","unstructured":"Bit-Monnot, A., Leofante, F., Pulina, L., Tacchella, A.: SMT-based planning for robots in smart factories. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds.) IEA\/AIE 2019. LNCS (LNAI), vol. 11606, pp. 674\u2013686. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-22999-3_58"},{"issue":"01","key":"34_CR6","first-page":"2727","volume":"33","author":"A Bunte","year":"2019","unstructured":"Bunte, A., Stein, B., Niggemann, O.: Model-based diagnosis for cyber-physical production systems based on machine learning and residual-based diagnosis models. Proc. AAAI Conf. Artif. Intell. 33(01), 2727\u20132735 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Cashmore, M., Fox, M., Long, D., Magazzeni, D.: A compilation of the full PDDL+ language into SMT. In: Workshops at the Thirtieth AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/icaps.v26i1.13755"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Ehrhardt, J., Ramonat, M., Heesch, R., Balzereit, K., Diedrich, A., Niggemann, O.: An AI benchmark for diagnosis, reconfiguration & planning. In: 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1\u20138. IEEE (2022)","DOI":"10.1109\/ETFA52439.2022.9921546"},{"key":"34_CR9","doi-asserted-by":"publisher","unstructured":"ElMaraghy, H.A.: Changing and evolving products and systems \u2013 models and enablers. In: Springer Series in Advanced Manufacturing, pp. 25\u201345. Springer, London (2009). https:\/\/doi.org\/10.1007\/978-1-84882-067-8_2","DOI":"10.1007\/978-1-84882-067-8_2"},{"key":"34_CR10","unstructured":"Ferber, P., Helmert, M., Hoffmann, J.: Neural network heuristics for classical planning: a study of hyperparameter space. In: European Conference on Artificial Intelligence (2020)"},{"key":"34_CR11","unstructured":"Ghallab, M., et al.: PDDL - the planning domain definition language. Technical Report CVC TR-98-003\/DCS TR-1165 (1998)"},{"key":"34_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2019.05.037","volume":"130","author":"S Goldrick","year":"2019","unstructured":"Goldrick, S., Duran-Villalobos, C.A., Jankauskas, K., Lovett, D., Farid, S.S., Lennox, B.: Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process. Comput. Chem. Eng. 130, 106471 (2019)","journal-title":"Comput. Chem. Eng."},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Grand, M., Pellier, D., Fiorino, H.: TempAMLSI: temporal action model learning based on STRIPS translation. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 32, 597\u2013605 (2022)","DOI":"10.1609\/icaps.v32i1.19847"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Hartung, F., et al.: Deep anomaly detection on tennessee eastman process data (2023)","DOI":"10.1002\/cite.202200238"},{"key":"34_CR15","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1613\/jair.855","volume":"14","author":"J Hoffmann","year":"2001","unstructured":"Hoffmann, J., Nebel, B.: The FF planning system: fast plan generation through heuristic search. J. Artif. Intell. Res. 14, 253\u2013302 (2001)","journal-title":"J. Artif. Intell. Res."},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Kagermann, H., Helbig, J., Hellinger, A., Wahlster, W.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion (2013)","DOI":"10.3390\/sci4030026"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"K\u00f6cher, A., et al.: A research agenda for AI planning in the field of flexible production systems. In: 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 1\u20138 (2022)","DOI":"10.1109\/ICPS51978.2022.9816866"},{"key":"34_CR18","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. In: Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)"},{"key":"34_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1007\/978-3-030-24311-1_38","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2019","author":"A Milani","year":"2019","unstructured":"Milani, A., Niyogi, R., Biondi, G.: Neural network based approach for learning planning action models. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11624, pp. 526\u2013537. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-24311-1_38"},{"key":"34_CR20","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.procir.2014.03.115","volume":"17","author":"L Monostori","year":"2014","unstructured":"Monostori, L.: Cyber-physical production systems: roots, expectations and r &d challenges. Procedia CIRP 17, 9\u201313 (2014)","journal-title":"Procedia CIRP"},{"key":"34_CR21","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.procir.2021.03.075","volume":"99","author":"T M\u00fcller","year":"2021","unstructured":"M\u00fcller, T., Jazdi, N., Schmidt, J.P., Weyrich, M.: Cyber-physical production systems: enhancement with a self-organized reconfiguration management. Procedia CIRP 99, 549\u2013554 (2021)","journal-title":"Procedia CIRP"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Multaheb, S., Bauer, F., Bretschneider, P., Niggemann, O.: Learning physically meaningful representations of energy systems with variational autoencoders. In: 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1\u20136 (2022)","DOI":"10.1109\/ETFA52439.2022.9921550"},{"key":"34_CR23","unstructured":"Nautrup, H.P., et al.: Operationally meaningful representations of physical systems in neural networks. Mach. Learn.: Sci. Technol. (2022)"},{"issue":"10","key":"34_CR24","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1515\/auto-2015-0060","volume":"63","author":"O Niggemann","year":"2015","unstructured":"Niggemann, O., Frey, C.: Data-driven anomaly detection in cyber-physical production systems. At - Automatisierungstechnik 63(10), 821\u2013832 (2015)","journal-title":"At - Automatisierungstechnik"},{"key":"34_CR25","unstructured":"van den Oord, A., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)"},{"key":"34_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1613\/jair.1.11633","volume":"68","author":"S Toyer","year":"2020","unstructured":"Toyer, S., Thi\u00e9baux, S., Trevizan, F., Xie, L.: ASNets: deep learning for generalised planning. J. Artif. Intell. Res. 68, 1\u201368 (2020)","journal-title":"J. Artif. Intell. Res."},{"key":"34_CR27","unstructured":"Zhang, Y., Lee, K., Lee, H.: Augmenting supervised neural networks with unsupervised objectives for large-scale image classification. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, vol. 48, pp. 612\u2013621. ICML\u201916, JMLR.org (2016)"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence. ECAI 2023 International Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50485-3_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T06:08:45Z","timestamp":1706076525000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50485-3_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031504846","9783031504853"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50485-3_34","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krak\u00f3w","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","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":"30 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecai2023.eu\/","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":"EasyChair and SMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","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":"69","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":"51% - 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":"2.86","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":"2.15","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Presented figures are relevant for the ECAI 2023 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)"}}]}}