{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:35:52Z","timestamp":1726043752441},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304898"},{"type":"electronic","value":"9783030304904"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30490-4_44","type":"book-chapter","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:02:47Z","timestamp":1567969367000},"page":"543-555","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Incorporating Adaptive RNN-Based Action Inference and Sensory Perception"],"prefix":"10.1007","author":[{"given":"Sebastian","family":"Otte","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jakob","family":"Stoll","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin V.","family":"Butz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"issue":"3","key":"44_CR1","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.cognition.2008.08.011","volume":"113","author":"M Botvinick","year":"2009","unstructured":"Botvinick, M., Niv, Y., Barto, A.C.: Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition 113(3), 262\u2013280 (2009). \n                      https:\/\/doi.org\/10.1016\/j.cognition.2008.08.011","journal-title":"Cognition"},{"issue":"1655","key":"44_CR2","doi-asserted-by":"publisher","first-page":"20130480","DOI":"10.1098\/rstb.2013.0480","volume":"369","author":"Matthew Botvinick","year":"2014","unstructured":"Botvinick, M., Weinstein, A.: Model-based hierarchical reinforcement learning and human action control. Philos. Trans. Roy. Soc. London B: Biol. Sci. 369(1655) (2014). \n                      https:\/\/doi.org\/10.1098\/rstb.2013.0480","journal-title":"Philosophical Transactions of the Royal Society B: Biological Sciences"},{"key":"44_CR3","doi-asserted-by":"publisher","unstructured":"Butz, M.V.: Towards a unified sub-symbolic computational theory of cognition. Front. Psychol. 7(925) (2016). \n                      https:\/\/doi.org\/10.3389\/fpsyg.2016.00925","DOI":"10.3389\/fpsyg.2016.00925"},{"key":"44_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.05.001","author":"MV Butz","year":"2019","unstructured":"Butz, M.V., Bilkey, D., Humaidan, D., Knott, A., Otte, S.: Learning, planning, and control in a monolithic neural event inference architecture. Neural Networks (2019). \n                      https:\/\/doi.org\/10.1016\/j.neunet.2019.05.001","journal-title":"Neural Networks"},{"key":"44_CR5","unstructured":"Butz, M.V., Bilkey, D., Knott, A., Otte, S.: Reprise: a retrospective and prospective inference scheme. In: Proceedings of the 40th Annual Meeting of the Cognitive Science Society, pp. 1427\u20131432 (2018)"},{"key":"44_CR6","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780198739692.001.0001","volume-title":"How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective","author":"MV Butz","year":"2017","unstructured":"Butz, M.V., Kutter, E.F.: How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective. Oxford University Press, Oxford (2017)"},{"key":"44_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-3398-8","volume-title":"Model Predictive Control","author":"EF Camacho","year":"1999","unstructured":"Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (1999). \n                      https:\/\/doi.org\/10.1007\/978-1-4471-3398-8"},{"issue":"7","key":"44_CR8","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.tics.2009.04.005","volume":"13","author":"K Friston","year":"2009","unstructured":"Friston, K.: The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13(7), 293\u2013301 (2009)","journal-title":"Trends Cogn. Sci."},{"issue":"1","key":"44_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/NECO_a_00912","volume":"29","author":"K Friston","year":"2016","unstructured":"Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., Pezzulo, G.: Active inference: a process theory. Neural Comput. 29(1), 1\u201349 (2016)","journal-title":"Neural Comput."},{"issue":"8","key":"44_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). \n                      https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"44_CR11","unstructured":"Kingma, D.P., Ba, J.L.: Adam: A method for stochastic optimization. In: 3rd International Conference for Learning Representations, abs\/1412.6980 (2015)"},{"key":"44_CR12","doi-asserted-by":"publisher","first-page":"e253","DOI":"10.1017\/S0140525X16001837","volume":"40","author":"BM Lake","year":"2017","unstructured":"Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J.: Building machines that learn and think like people. Behav. Brain Sci. 40, e253 (2017). \n                      https:\/\/doi.org\/10.1017\/S0140525X16001837","journal-title":"Behav. Brain Sci."},{"issue":"7540","key":"44_CR13","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). \n                      https:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"key":"44_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1007\/978-3-030-01424-7_73","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"S Otte","year":"2018","unstructured":"Otte, S., Hofmaier, L., Butz, M.V.: Integrative collision avoidance within RNN-driven many-joint robot arms. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 748\u2013758. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-01424-7_73"},{"key":"44_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/978-3-319-68600-4_27","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2017","author":"S Otte","year":"2017","unstructured":"Otte, S., Schmitt, T., Friston, K., Butz, M.V.: Inferring adaptive goal-directed behavior within recurrent neural networks. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10613, pp. 227\u2013235. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-68600-4_27"},{"key":"44_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-319-68600-4_31","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2017","author":"S Otte","year":"2017","unstructured":"Otte, S., Zwiener, A., Butz, M.V.: Inherently constraint-aware control of many-joint robot arms with inverse recurrent models. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10613, pp. 262\u2013270. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-68600-4_31"},{"key":"44_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-319-44778-0_18","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2016","author":"S Otte","year":"2016","unstructured":"Otte, S., Zwiener, A., Hanten, R., Zell, A.: Inverse recurrent models \u2013 an application scenario for many-joint robot arm control. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9886, pp. 149\u2013157. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-44778-0_18"},{"issue":"5","key":"44_CR18","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1109\/TNN.1998.712192","volume":"9","author":"R.S. Sutton","year":"1998","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction (1998)","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"10","key":"44_CR19","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/5.58337","volume":"78","author":"P Werbos","year":"1990","unstructured":"Werbos, P.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550\u20131560 (1990). \n                      https:\/\/doi.org\/10.1109\/5.58337","journal-title":"Proc. IEEE"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Text and Time Series"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30490-4_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:11:50Z","timestamp":1567969910000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30490-4_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304898","9783030304904"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30490-4_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}