{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T12:00:49Z","timestamp":1774872049870,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030595340","type":"print"},{"value":"9783030595357","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-59535-7_9","type":"book-chapter","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T07:03:52Z","timestamp":1600671832000},"page":"117-133","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Designing a Neural Network Primitive for Conditional Structural Transformations"],"prefix":"10.1007","author":[{"given":"Alexander","family":"Demidovskij","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduard","family":"Babkin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"9_CR1","unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). http:\/\/tensorflow.org\/ . Software available from tensorflow.org"},{"key":"9_CR2","unstructured":"Besold, T.R., et al.: Neural-symbolic learning and reasoning: a survey and interpretation. arXiv preprint arXiv:1711.03902 (2017)"},{"key":"9_CR3","first-page":"97","volume":"14","author":"TR Besold","year":"2015","unstructured":"Besold, T.R., K\u00fchnberger, K.U.: Towards integrated neural-symbolic systems for human-level AI: two research programs helping to bridge the gaps. Biol. Inspired Cogn. Archit. 14, 97\u2013110 (2015)","journal-title":"Biol. Inspired Cogn. Archit."},{"issue":"10","key":"9_CR4","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1016\/S0893-6080(01)00109-5","volume":"14","author":"A Browne","year":"2001","unstructured":"Browne, A., Sun, R.: Connectionist inference models. Neural Netw. 14(10), 1331\u20131355 (2001)","journal-title":"Neural Netw."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, P., Zhou, B., Chen, Z., Tan, J.: The topsis method for decision making with 2-tuple linguistic intuitionistic fuzzy sets. In: 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1603\u20131607. IEEE (2017)","DOI":"10.1109\/IAEAC.2017.8054284"},{"issue":"1","key":"9_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1515\/lingvan-2016-0105","volume":"3","author":"PW Cho","year":"2017","unstructured":"Cho, P.W., Goldrick, M., Smolensky, P.: Incremental parsing in a continuous dynamical system: sentence processing in gradient symbolic computation. Linguistics Vanguard 3(1), 1\u201310 (2017)","journal-title":"Linguistics Vanguard"},{"key":"9_CR7","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"9_CR8","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-030-29516-5_9","volume-title":"Intelligent Systems and Applications","author":"A Demidovskij","year":"2020","unstructured":"Demidovskij, A.: Implementation aspects of tensor product variable binding in connectionist systems. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) IntelliSys 2019. AISC, vol. 1037, pp. 97\u2013110. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-29516-5_9"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Demidovskij, A.: Automatic construction of tensor product variable binding neural networks for neural-symbolic intelligent systems. In: Proceedings of 2nd International Conference on Electrical, Communication and Computer Engineering, pp. not published, accepted. IEEE (2020)","DOI":"10.1109\/ICECCE49384.2020.9179403"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Demidovskij, A., Babkin, E.: Developing a distributed linguistic decision making system. Business Informatics 13(1 (eng)) (2019)","DOI":"10.17323\/1998-0663.2019.1.18.32"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Demidovskij, A., Babkin, E.: Towards designing linguistic assessments aggregation as a distributed neuroalgorithm. In: 2020 XXII International Conference on Soft Computing and Measurements (SCM)), pp. not published, accepted. IEEE (2020)","DOI":"10.1109\/SCM50615.2020.9198767"},{"key":"9_CR12","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/978-3-030-30425-6_44","volume-title":"Advances in Neural Computation, Machine Learning, and Cognitive Research III","author":"AV Demidovskij","year":"2020","unstructured":"Demidovskij, A.V.: Towards automatic manipulation of arbitrary structures in connectivist paradigm with tensor product variable binding. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds.) NEUROINFORMATICS 2019. SCI, vol. 856, pp. 375\u2013383. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-30425-6_44"},{"key":"9_CR13","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"1\u20132","key":"9_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0010-0277(88)90031-5","volume":"28","author":"JA Fodor","year":"1988","unstructured":"Fodor, J.A., Pylyshyn, Z.W., et al.: Connectionism and cognitive architecture: a critical analysis. Cognition 28(1\u20132), 3\u201371 (1988)","journal-title":"Cognition"},{"issue":"8","key":"9_CR15","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1162\/NECO_a_00467","volume":"25","author":"SI Gallant","year":"2013","unstructured":"Gallant, S.I., Okaywe, T.W.: Representing objects, relations, and sequences. Neural Comput. 25(8), 2038\u20132078 (2013)","journal-title":"Neural Comput."},{"issue":"2","key":"9_CR16","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/j.ijpe.2011.01.015","volume":"131","author":"D Golmohammadi","year":"2011","unstructured":"Golmohammadi, D.: Neural network application for fuzzy multi-criteria decision making problems. Int. J. Prod. Econ. 131(2), 490\u2013504 (2011)","journal-title":"Int. J. Prod. Econ."},{"key":"9_CR17","doi-asserted-by":"publisher","unstructured":"Huang, Q., Smolensky, P., He, X., Deng, L., Wu, D.: Tensor product generation networks for deep NLP modeling. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 1263\u20131273. Association for Computational Linguistics, New Orleans (2018). https:\/\/doi.org\/10.18653\/v1\/N18-1114 . https:\/\/www.aclweb.org\/anthology\/N18-1114","DOI":"10.18653\/v1\/N18-1114"},{"key":"9_CR18","unstructured":"Legendre, G., Miyata, Y., Smolensky, P.: Distributed recursive structure processing. In: Advances in Neural Information Processing Systems, pp. 591\u2013597 (1991)"},{"issue":"1\u20132","key":"9_CR19","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s11229-005-9086-5","volume":"146","author":"H Leitgeb","year":"2005","unstructured":"Leitgeb, H.: Interpreted dynamical systems and qualitative laws: from neural networks to evolutionary systems. Synthese 146(1\u20132), 189\u2013202 (2005)","journal-title":"Synthese"},{"key":"9_CR20","unstructured":"McCoy, R.T., Linzen, T., Dunbar, E., Smolensky, P.: RNNS implicitly implement tensor product representations. arXiv preprint arXiv:1812.08718 (2018)"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Palangi, H., Smolensky, P., He, X., Deng, L.: Question-answering with grammatically-interpretable representations. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12004"},{"key":"9_CR22","unstructured":"de Penning, H.L.H., Garcez, A.S.d., Lamb, L.C., Meyer, J.J.C.: A neural-symbolic cognitive agent for online learning and reasoning. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)"},{"issue":"2","key":"9_CR23","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/0004-3702(94)00032-V","volume":"77","author":"G Pinkas","year":"1995","unstructured":"Pinkas, G.: Reasoning, nonmonotonicity and learning in connectionist networks that capture propositional knowledge. Artif. Intell. 77(2), 203\u2013247 (1995)","journal-title":"Artif. Intell."},{"key":"9_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1007\/978-3-642-33269-2_61","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2012","author":"G Pinkas","year":"2012","unstructured":"Pinkas, G., Lima, P., Cohen, S.: A dynamic binding mechanism for retrieving and unifying complex predicate-logic knowledge. In: Villa, A.E.P., Duch, W., \u00c9rdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7552, pp. 482\u2013490. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33269-2_61"},{"key":"9_CR25","first-page":"87","volume":"6","author":"G Pinkas","year":"2013","unstructured":"Pinkas, G., Lima, P., Cohen, S.: Representing, binding, retrieving and unifying relational knowledge using pools of neural binders. Biol. Inspired Cogn. Archit. 6, 87\u201395 (2013)","journal-title":"Biol. Inspired Cogn. Archit."},{"key":"9_CR26","unstructured":"Serafini, L., Garcez, A.d.: Logic tensor networks: deep learning and logical reasoning from data and knowledge. arXiv preprint arXiv:1606.04422 (2016)"},{"issue":"1\u20132","key":"9_CR27","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/0004-3702(90)90007-M","volume":"46","author":"P Smolensky","year":"1990","unstructured":"Smolensky, P.: Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artif. Intell. 46(1\u20132), 159\u2013216 (1990)","journal-title":"Artif. Intell."},{"issue":"6","key":"9_CR28","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1111\/cogs.12047","volume":"38","author":"P Smolensky","year":"2014","unstructured":"Smolensky, P., Goldrick, M., Mathis, D.: Optimization and quantization in gradient symbol systems: a framework for integrating the continuous and the discrete in cognition. Cogn. Sci. 38(6), 1102\u20131138 (2014)","journal-title":"Cogn. Sci."},{"key":"9_CR29","unstructured":"Smolensky, P., Legendre, G.: The Harmonic Mind: From Neural Computation to Optimality-theoretic Grammar (Cognitive Architecture), Vol. 1. MIT press, Cambridge (2006)"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Soulos, P., McCoy, T., Linzen, T., Smolensky, P.: Discovering the compositional structure of vector representations with role learning networks. arXiv preprint arXiv:1910.09113 (2019)","DOI":"10.18653\/v1\/2020.blackboxnlp-1.23"},{"key":"9_CR31","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.artint.2015.04.002","volume":"244","author":"S Teso","year":"2017","unstructured":"Teso, S., Sebastiani, R., Passerini, A.: Structured learning modulo theories. Artif. Intell. 244, 166\u2013187 (2017)","journal-title":"Artif. Intell."},{"issue":"6","key":"9_CR32","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1002\/int.21798","volume":"31","author":"C Wei","year":"2016","unstructured":"Wei, C., Liao, H.: A multigranularity linguistic group decision-making method based on hesitant 2-tuple sets. Int. J. Intell. Syst. 31(6), 612\u2013634 (2016)","journal-title":"Int. J. Intell. Syst."},{"key":"9_CR33","unstructured":"Yousefpour, A., et al.: Failout: achieving failure-resilient inference in distributed neural networks. arXiv preprint arXiv:2002.07386 (2020)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59535-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T08:49:08Z","timestamp":1668847748000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-59535-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030595340","9783030595357"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59535-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"22 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russian Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Moscow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rcai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/caics.ru\/en_raai","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":"140","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":"27","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":"8","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":"19% - 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":"2.8","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":"6","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)"}}]}}