{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:29:03Z","timestamp":1743118143173,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030938413"},{"type":"electronic","value":"9783030938420"}],"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\/978-3-030-93842-0_4","type":"book-chapter","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T18:25:30Z","timestamp":1641925530000},"page":"69-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Task Independent Capsule-Based Agents for Deep Q-Learning"],"prefix":"10.1007","author":[{"given":"Akash","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tom","family":"De Schepper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Mets","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Hellinckx","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Oramas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven","family":"Latr\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1016\/j.patrec.2020.09.010","volume":"138","author":"P Afshar","year":"2020","unstructured":"Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K.N., Mohammadi, A.: COVID-CAPS: a capsule network-based framework for identification of COVID-19 cases from X-ray images. Pattern Recogn. Lett. 138, 638\u2013643 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"Afshar, P., Plataniotis, K.N., Mohammadi, A.: Capsule networks for brain tumor classification based on MRI images and course tumor boundaries. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1368\u20131372, November 2019","DOI":"10.1109\/ICASSP.2019.8683759"},{"key":"4_CR3","doi-asserted-by":"publisher","unstructured":"Allioui, H., Sadgal, M., Elfazziki, A.: Deep MRI segmentation: a convolutional method applied to Alzheimer disease detection. Int. J. Adv. Comput. Sci. Appl. 10(11) (2019). https:\/\/doi.org\/10.14569\/IJACSA.2019.0101151","DOI":"10.14569\/IJACSA.2019.0101151"},{"key":"4_CR4","unstructured":"Andersen, P.A.: Deep reinforcement learning using capsules in advanced game environments. arXiv:1801.09597 [cs, stat], January 2018"},{"key":"4_CR5","unstructured":"Bahadori, M.T.: Spectral capsule networks, p. 5 (2018). https:\/\/openreview.net\/forum?id=HJuMvYPaM"},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1613\/jair.3912","volume":"47","author":"MG Bellemare","year":"2013","unstructured":"Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: an evaluation platform for general agents. J. Artif. Intell. Res. 47, 253\u2013279 (2013). https:\/\/doi.org\/10.1613\/jair.3912","journal-title":"J. Artif. Intell. Res."},{"key":"4_CR7","unstructured":"Eck, D.J.: Introduction to Computer Graphics (2016)"},{"key":"4_CR8","unstructured":"Gou, S.Z., Liu, Y.: DQN with model-based exploration: efficient learning on environments with sparse rewards. arXiv:1903.09295 [cs, stat], March 2019"},{"key":"4_CR9","unstructured":"Hinton, G., Sabour, S., Frosst, N.: Matrix capsules with EM routing. In: International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=HJWLfGWRb"},{"issue":"1","key":"4_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"W Huang","year":"2020","unstructured":"Huang, W., Zhou, F.: DA-CapsNet: dual attention mechanism capsule network. Sci. Rep. 10(1), 1\u201313 (2020)","journal-title":"Sci. Rep."},{"issue":"3","key":"4_CR11","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1113\/jphysiol.1963.sp007079","volume":"165","author":"DH Hubel","year":"1963","unstructured":"Hubel, D.H., Wiesel, T.N.: Shape and arrangement of columns in cat\u2019s striate cortex. J. Physiol. 165(3), 559\u2013568 (1963). https:\/\/doi.org\/10.1113\/jphysiol.1963.sp007079","journal-title":"J. Physiol."},{"key":"4_CR12","doi-asserted-by":"publisher","unstructured":"Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 655\u2013665. Association for Computational Linguistics, Baltimore (2014). https:\/\/doi.org\/10.3115\/v1\/P14-1062","DOI":"10.3115\/v1\/P14-1062"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Kempka, M., Wydmuch, M., Runc, G., Toczek, J., Ja\u015bkowski, W.: ViZDoom: a doom-based AI research platform for visual reinforcement learning. In: 2016 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1\u20138. IEEE (2016)","DOI":"10.1109\/CIG.2016.7860433"},{"issue":"6","key":"4_CR14","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"key":"4_CR15","unstructured":"LaLonde, R., Bagci, U.: Capsules for object segmentation. arXiv:1804.04241 [cs, stat], April 2018"},{"key":"4_CR16","unstructured":"Liao, H.: CapsNet-Tensorflow (2018). https:\/\/github.com\/naturomics\/CapsNet-Tensorflow\/blob\/master\/imgs\/capsuleVSneuron.png"},{"key":"4_CR17","unstructured":"Martnez-Plumed, F., Hernandez-Orallo, J.: AI results for the Atari 2600 games: difficulty and discrimination using IRT. In: Evaluating General-Purpose AI, p. 6 (2017)"},{"key":"4_CR18","unstructured":"Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv:1312.5602 [cs], December 2013"},{"issue":"7540","key":"4_CR19","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). https:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"issue":"1","key":"4_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijaia.2020.11101","volume":"11","author":"T Molnar","year":"2020","unstructured":"Molnar, T., Culurciello, E.: Capsule network performance with autonomous navigation. Int. J. Artif. Intell. Appl. 11(1), 1\u201315 (2020). https:\/\/doi.org\/10.5121\/ijaia.2020.11101","journal-title":"Int. J. Artif. Intell. Appl."},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Pan, C., Velipasalar, S.: PT-CapsNet: a novel prediction-tuning capsule network suitable for deeper architectures. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11996\u201312005 (2021)","DOI":"10.1109\/ICCV48922.2021.01178"},{"issue":"4","key":"4_CR22","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.conb.2008.09.008","volume":"18","author":"DG Pelli","year":"2008","unstructured":"Pelli, D.G.: Crowding: a cortical constraint on object recognition. Curr. Opin. Neurobiol. 18(4), 445\u2013451 (2008). https:\/\/doi.org\/10.1016\/j.conb.2008.09.008","journal-title":"Curr. Opin. Neurobiol."},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Phaye, S.S.R., Sikka, A., Dhall, A., Bathula, D.: Dense and diverse capsule networks: making the capsules learn better. arXiv:1805.04001 [cs], May 2018","DOI":"10.1007\/978-3-030-20873-8_37"},{"key":"4_CR24","unstructured":"Rawlinson, D., Ahmed, A., Kowadlo, G.: Sparse unsupervised capsules generalize better. arXiv:1804.06094 [cs], April 2018"},{"key":"4_CR25","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 3856\u20133866. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/6975-dynamic-routing-between-capsules.pdf"},{"key":"4_CR26","unstructured":"Schaul, T., Quan, J., Antonoglou, I., Silver, D.: Prioritized experience replay. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2\u20134 May 2016, Conference Track Proceedings (2016). http:\/\/arxiv.org\/abs\/1511.05952"},{"key":"4_CR27","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":"4_CR28","doi-asserted-by":"crossref","unstructured":"van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double Q-learning. arXiv:1509.06461 [cs], December 2015","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"4_CR29","doi-asserted-by":"publisher","first-page":"8855","DOI":"10.1109\/TIP.2020.3019925","volume":"29","author":"X Wen","year":"2020","unstructured":"Wen, X., Han, Z., Liu, X., Liu, Y.S.: Point2SpatialCapsule: aggregating features and spatial relationships of local regions on point clouds using spatial-aware capsules. IEEE Trans. Image Process. 29, 8855\u20138869 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"4_CR30","doi-asserted-by":"publisher","unstructured":"Wu, Y., Ma, S., Zhang, D., Sun, J.: 3D capsule hand pose estimation network based on structural relationship information. Symmetry 12(10) (2020). https:\/\/doi.org\/10.3390\/sym12101636. https:\/\/www.mdpi.com\/2073-8994\/12\/10\/1636","DOI":"10.3390\/sym12101636"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93842-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T02:36:36Z","timestamp":1726454196000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93842-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030938413","9783030938420"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93842-0_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BNAIC\/Benelearn","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Benelux Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Esch-sur-Alzette","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Luxembourg","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bnaic2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bnaic2021.uni.lu\/","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":"46","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":"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":"30% - 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","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)"}}]}}