{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T01:31:25Z","timestamp":1771637485503,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031084201","type":"print"},{"value":"9783031084218","type":"electronic"}],"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\/978-3-031-08421-8_11","type":"book-chapter","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T21:02:38Z","timestamp":1658178158000},"page":"153-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Option Discovery for Autonomous Generation of Symbolic Knowledge"],"prefix":"10.1007","author":[{"given":"Gabriele","family":"Sartor","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davide","family":"Zollo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marta","family":"Cialdea Mayer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angelo","family":"Oddi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riccardo","family":"Rasconi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vieri Giuliano","family":"Santucci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3389\/fnbot.2019.00045","volume":"13","author":"G Baldassarre","year":"2019","unstructured":"Baldassarre, G., Lord, W., Granato, G., Santucci, V.G.: An embodied agent learning affordances with intrinsic motivations and solving extrinsic tasks with attention and one-step planning. Front. Neurorobot. 13, 45 (2019)","journal-title":"Front. Neurorobot."},{"key":"11_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32375-1","volume-title":"Intrinsically Motivated Learning in Natural and Artificial Systems","year":"2013","unstructured":"Baldassarre, G., Mirolli, M. (eds.): Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-32375-1"},{"issue":"1","key":"11_CR3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.robot.2012.05.008","volume":"61","author":"A Baranes","year":"2013","unstructured":"Baranes, A., Oudeyer, P.Y.: Active learning of inverse models with intrinsically motivated goal exploration in robots. Robot. Auton. Syst. 61(1), 49\u201373 (2013)","journal-title":"Robot. Auton. Syst."},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1022140919877","volume":"13","author":"AG Barto","year":"2003","unstructured":"Barto, A.G., Mahadevan, S.: Recent advances in hierarchical reinforcement learning. Disc. Event Dyn. Syst. 13(1), 41\u201377 (2003)","journal-title":"Disc. Event Dyn. Syst."},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.neucom.2019.10.124","volume":"452","author":"JA Becerra","year":"2021","unstructured":"Becerra, J.A., Romero, A., Bellas, F., Duro, R.J.: Motivational engine and long-term memory coupling within a cognitive architecture for lifelong open-ended learning. Neurocomputing 452, 341\u2013354 (2021)","journal-title":"Neurocomputing"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41\u201348 (2009)","DOI":"10.1145\/1553374.1553380"},{"issue":"1","key":"11_CR7","first-page":"933","volume":"24","author":"B Bonet","year":"2005","unstructured":"Bonet, B., Geffner, H.: mGPT: a probabilistic planner based on heuristic search. J. Artif. Int. Res. 24(1), 933\u2013944 (2005)","journal-title":"J. Artif. Int. Res."},{"key":"11_CR8","unstructured":"Colas, C., Fournier, P., Chetouani, M., Sigaud, O., Oudeyer, P.Y.: CURIOUS: intrinsically motivated modular multi-goal reinforcement learning. In: International Conference on Machine Learning, pp. 1331\u20131340. PMLR (2019)"},{"issue":"3","key":"11_CR9","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995). https:\/\/doi.org\/10.1023\/A:1022627411411","journal-title":"Mach. Learn."},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"59","DOI":"10.3389\/fnbot.2018.00059","volume":"12","author":"S Doncieux","year":"2018","unstructured":"Doncieux, S., et al.: Open-ended learning: a conceptual framework based on representational redescription. Front. Neurorobot. 12, 59 (2018)","journal-title":"Front. Neurorobot."},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-020-02165-6","volume":"51","author":"M Ebrahimi","year":"2021","unstructured":"Ebrahimi, M., Eberhart, A., Bianchi, F., Hitzler, P.: Towards bridging the neuro-symbolic gap: deep deductive reasoners. Appl. Intell. 51, 1\u201323 (2021)","journal-title":"Appl. Intell."},{"key":"11_CR12","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD 1996, pp. 226\u2013231. AAAI Press (1996)"},{"key":"11_CR13","unstructured":"Forestier, S., Portelas, R., Mollard, Y., Oudeyer, P.Y.: Intrinsically motivated goal exploration processes with automatic curriculum learning. arXiv preprint arXiv:1708.02190 (2017)"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fnbot.2013.00025","volume":"7","author":"M Frank","year":"2014","unstructured":"Frank, M., Leitner, J., Stollenga, M., F\u00f6rster, A., Schmidhuber, J.: Curiosity driven reinforcement learning for motion planning on humanoids. Front. Neurorobot. 7, 25 (2014)","journal-title":"Front. Neurorobot."},{"key":"11_CR15","unstructured":"Hayamizu, Y., Amiri, S., Chandan, K., Takadama, K., Zhang, S.: Guiding robot exploration in reinforcement learning via automated planning. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 31, no. 1, pp. 625\u2013633 (2021), https:\/\/ojs.aaai.org\/index.php\/ICAPS\/article\/view\/16011"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Konidaris, G., Kaelbling, L.P., Lozano-Perez, T.: From skills to symbols: learning symbolic representations for abstract high-level planning. J. Artif. Intell. Res. 61, 215\u2013289 (2018). http:\/\/lis.csail.mit.edu\/pubs\/konidaris-jair18.pdf","DOI":"10.1613\/jair.5575"},{"key":"11_CR17","first-page":"3675","volume":"29","author":"TD Kulkarni","year":"2016","unstructured":"Kulkarni, T.D., Narasimhan, K., Saeedi, A., Tenenbaum, J.: Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. Adv. Neural. Inf. Process. Syst. 29, 3675\u20133683 (2016)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR18","volume-title":"Automated Planning: Theory & Practice","author":"D Nau","year":"2004","unstructured":"Nau, D., Ghallab, M., Traverso, P.: Automated Planning: Theory & Practice. Morgan Kaufmann Publishers Inc., San Francisco (2004)"},{"key":"11_CR19","unstructured":"Oddi, A., et al.: Integrating open-ended learning in the sense-plan-act robot control paradigm. In: ECAI 2020, the 24th European Conference on Artificial Intelligence (2020)"},{"key":"11_CR20","unstructured":"Oddi, A., et al.: An intrinsically motivated planning architecture for curiosity-driven robots. In: 6th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2019, vol. 2594, pp. 19\u201324. CEUR-WS (2020)"},{"issue":"2","key":"11_CR21","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1109\/TEVC.2006.890271","volume":"11","author":"PY Oudeyer","year":"2007","unstructured":"Oudeyer, P.Y., Kaplan, F., Hafner, V.: Intrinsic motivation systems for autonomous mental development. IEEE Trans. Evol. Comput. 11(2), 265\u2013286 (2007)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065\u20131076 (1962). http:\/\/www.jstor.org\/stable\/2237880","DOI":"10.1214\/aoms\/1177704472"},{"issue":"null","key":"11_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(null), 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Rafati, J., Noelle, D.C.: Learning representations in model-free hierarchical reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 10009\u201310010 (2019)","DOI":"10.1609\/aaai.v33i01.330110009"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Romero, A., Baldassarre, G., Duro, R.J., Santucci, V.G.: Analysing autonomous open-ended learning of skills with different interdependent subgoals in robots. In: 2021 20th International Conference on Advanced Robotics (ICAR), pp. 646\u2013651. IEEE (2021)","DOI":"10.1109\/ICAR53236.2021.9659371"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Rosenblatt, M.: Remarks on some nonparametric estimates of a density function. Ann. Math. Stat. 27(3), 832\u2013837 (1956). http:\/\/www.jstor.org\/stable\/2237390","DOI":"10.1214\/aoms\/1177728190"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Santucci, V.G., Baldassarre, G., Cartoni, E.: Autonomous reinforcement learning of multiple interrelated tasks. In: 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 221\u2013227. IEEE (2019)","DOI":"10.1109\/DEVLRN.2019.8850713"},{"key":"11_CR28","unstructured":"Santucci, V.G., Baldassarre, G., Mirolli, M.: Biological cumulative learning through intrinsic motivations: a simulated robotic study on development of visually-guided reaching. In: Proceedings of the Tenth International Conference on Epigenetic Robotics (EpiRob2010), pp. 121\u2013128 (2010)"},{"issue":"3","key":"11_CR29","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1109\/TCDS.2016.2538961","volume":"8","author":"VG Santucci","year":"2016","unstructured":"Santucci, V.G., Baldassarre, G., Mirolli, M.: Grail: a goal-discovering robotic architecture for intrinsically-motivated learning. IEEE Trans. Cognitive Dev. Syst. 8(3), 214\u2013231 (2016)","journal-title":"IEEE Trans. Cognitive Dev. Syst."},{"key":"11_CR30","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3389\/fnbot.2019.00115","volume":"13","author":"VG Santucci","year":"2020","unstructured":"Santucci, V.G., Oudeyer, P.Y., Barto, A., Baldassarre, G.: Intrinsically motivated open-ended learning in autonomous robots. Front. Neurorobot. 13, 115 (2020)","journal-title":"Front. Neurorobot."},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Seepanomwan, K., Santucci, V.G., Baldassarre, G.: Intrinsically motivated discovered outcomes boost user\u2019s goals achievement in a humanoid robot. In: 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 178\u2013183 (2017)","DOI":"10.1109\/DEVLRN.2017.8329804"},{"key":"11_CR32","unstructured":"Singh, S., Barto, A.G., Chentanez, N.: Intrinsically motivated reinforcement learning. In: Proceedings of the 17th International Conference on Neural Information Processing Systems, NIPS 2004, pp. 1281\u20131288. MIT Press, Cambridge (2004)"},{"key":"11_CR33","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1007\/3-540-45622-8_16","volume-title":"Abstraction, Reformulation, and Approximation","author":"M Stolle","year":"2002","unstructured":"Stolle, M., Precup, D.: Learning options in reinforcement learning. In: Koenig, S., Holte, R.C. (eds.) SARA 2002. LNCS (LNAI), vol. 2371, pp. 212\u2013223. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45622-8_16"},{"key":"11_CR34","volume-title":"Reinforcement Learning: An Introduction","author":"RS Sutton","year":"1998","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)"},{"key":"11_CR35","volume-title":"Reinforcement Learning: An Introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)"},{"key":"11_CR36","unstructured":"Sutton, R.S., Precup, D., Singh, S.: Intra-option learning about temporally abstract actions. In: Proceedings of 15th International Conference on Machine Learning, pp. 556\u2013564. Morgan Kaufmann, San Francisco (1998)"},{"key":"11_CR37","doi-asserted-by":"publisher","unstructured":"Sutton, R.S., Precup, D., Singh, S.: Between mdps and semi-mdps: a framework for temporal abstraction in reinforcement learning. Artif. Intell. 112(1-2), 181\u2013211 (1999). https:\/\/doi.org\/10.1016\/S0004-3702(99)00052-1","DOI":"10.1016\/S0004-3702(99)00052-1"},{"issue":"2","key":"11_CR38","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1109\/TAMD.2010.2050205","volume":"2","author":"CM Vigorito","year":"2010","unstructured":"Vigorito, C.M., Barto, A.G.: Intrinsically motivated hierarchical skill learning in structured environments. IEEE Trans. Auton. Ment. Dev. 2(2), 132\u2013143 (2010). https:\/\/doi.org\/10.1109\/TAMD.2010.2050205","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"11_CR39","unstructured":"Younes, H., Littman, M.: PPDDL1.0: an extension to PDDL for expressiong planning domains with probabilistic effects. Technical report, Carnegie Mellon University (2004). CMU-CS-04-167"}],"container-title":["Lecture Notes in Computer Science","AIxIA 2021 \u2013 Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08421-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T21:03:38Z","timestamp":1658178218000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08421-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031084201","9783031084218"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08421-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIxIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of the Italian Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiia2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aixia2021.disco.unimib.it\/","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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","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":"36","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":"62% - 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","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","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)"}}]}}