{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:22:46Z","timestamp":1743024166258,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031479571"},{"type":"electronic","value":"9783031479588"}],"license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"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\/978-3-031-47958-8_1","type":"book-chapter","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T18:01:56Z","timestamp":1700071316000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contextual Qualitative Deterministic Models for\u00a0Self-learning Embodied Agents"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2106-448X","authenticated-orcid":false,"given":"Jan","family":"Lemeire","sequence":"first","affiliation":[]},{"given":"Nick","family":"Wouters","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Van Cleemput","sequence":"additional","affiliation":[]},{"given":"Aron","family":"Heirman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"1_CR1","unstructured":"Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in Bayesian networks. In: Uncertainty in Artificial Intelligence, pp. 115\u2013123 (1996)"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Bratko, I.: An assessment of machine learning methods for robotic discovery. J. Comput. Inf. Technol. 16, 247\u2013254 (2008)","DOI":"10.2498\/cit.1001392"},{"key":"1_CR3","unstructured":"Bratko, I., Suc, D.: Learning qualitative models. AI Mag. 24, 107\u2013119 (2004)"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"\u00c7atal, O., Wauthier, S.T., De Boom, C., Verbelen, T., Dhoedt, B.: Learning generative state space models for active inference. Frontiers Comput. Neurosci. 14 (2020)","DOI":"10.3389\/fncom.2020.574372"},{"issue":"9","key":"1_CR5","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/j.apal.2019.04.004","volume":"170","author":"J Corander","year":"2019","unstructured":"Corander, J., Hyttinen, A., Kontinen, J., Pensar, J., V\u00e4\u00e4n\u00e4nen, J.: A logical approach to context-specific independence. Ann. Pure Appl. Logic 170(9), 975\u2013992 (2019)","journal-title":"Ann. Pure Appl. Logic"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Forbus, K.D.: Chapter 9 qualitative modeling. In: Foundations of Artificial Intelligence. Handbook of Knowledge Representation, vol. 3, pp. 361\u2013393. Elsevier, January 2008","DOI":"10.1016\/S1574-6526(07)03009-X"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Friston, K., Kilner, J., Harrison, L.: A free energy principle for the brain. J. Physiol. 100(1\u20133), 70\u201387 (2006)","DOI":"10.1016\/j.jphysparis.2006.10.001"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Konidaris, G., Kaelbling, L., Lozano-Perez, T.: Constructing symbolic representations for high-level planning. Proc. AAAI Conf. Artif. Intell. 28(1) (2014)","DOI":"10.1609\/aaai.v28i1.9004"},{"issue":"9","key":"1_CR9","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1016\/j.ijar.2012.06.004","volume":"53","author":"J Lemeire","year":"2012","unstructured":"Lemeire, J., Meganck, S., Cartella, F., Liu, T.: Conservative independence-based causal structure learning in absence of adjacency faithfulness. Int. J. Approx. Reasoning 53(9), 1305\u20131325 (2012)","journal-title":"Int. J. Approx. Reasoning"},{"issue":"1","key":"1_CR10","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/TAMD.2011.2160943","volume":"4","author":"J Mugan","year":"2012","unstructured":"Mugan, J., Kuipers, B.: Autonomous learning of high-level states and actions in continuous environments. IEEE Trans. Auton. Ment. Dev. 4(1), 70\u201386 (2012)","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"1_CR11","doi-asserted-by":"publisher","unstructured":"Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. 2nd edn. Springer, New York (1993). https:\/\/doi.org\/10.1007\/978-1-4612-2748-9","DOI":"10.1007\/978-1-4612-2748-9"},{"key":"1_CR12","unstructured":"Tikka, S., Hyttinen, A., Karvanen, J.: Identifying causal effects via context-specific independence relations. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"1_CR13","unstructured":"Zabkar, J., Bratko, I., Mohan, A.C.: Learning qualitative models by an autonomous robot. In: 22nd International Workshop on Qualitative Reasoning, pp. 150\u2013157 (2008)"}],"container-title":["Communications in Computer and Information Science","Active Inference"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47958-8_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T18:02:56Z","timestamp":1700071376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47958-8_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,16]]},"ISBN":["9783031479571","9783031479588"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47958-8_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,16]]},"assertion":[{"value":"16 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Active Inference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","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":"13 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":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwai-ws2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iwaiworkshop.github.io\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34","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":"17","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":"50% - 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":"3.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}