{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T18:51:51Z","timestamp":1767034311059,"version":"3.41.0"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T00:00:00Z","timestamp":1573776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"JST ERATO Ishiguro Symbiotic Human-Robot Interaction Project","award":["JPMJER1401"],"award-info":[{"award-number":["JPMJER1401"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Hum.-Robot Interact."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p>\n            Many recent studies have shown that behaviors and interaction logic for social robots can be learned automatically from natural examples of human-human interaction by machine learning algorithms, with minimal input from human designers [1--4]. In this work, we exceed the capabilities of the previous approaches by giving the robot\n            <jats:italic>memory<\/jats:italic>\n            . In earlier work, the robot's actions were decided based only on a narrow temporal window of the current interaction context. However, human behaviors often depend on more temporally distant events in the interaction history. Thus, we raise the question of whether (and how) an automated behavior learning system can learn a memory representation of interaction history within a simulated camera shop scenario. An analysis of the types of\n            <jats:italic>memory-setting<\/jats:italic>\n            and\n            <jats:italic>memory-dependent<\/jats:italic>\n            actions that occur in the camera shop scenario is presented. Then, to create more examples of such actions for evaluating a shopkeeper robot behavior learning system, an interaction dataset is simulated. A Gated Recurrent Unit (GRU) neural network architecture is applied in the behavior learning system, which learns a memory representation for performing memory-dependent actions. In an offline evaluation, the GRU system significantly outperformed a without-memory baseline system at generating appropriate memory-dependent actions. Finally, an analysis of the GRU architecture's memory representation is presented.\n          <\/jats:p>","DOI":"10.1145\/3338810","type":"journal-article","created":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T21:16:57Z","timestamp":1573852617000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Neural-network-based Memory for a Social Robot"],"prefix":"10.1145","volume":"8","author":[{"given":"Malcolm","family":"Doering","sequence":"first","affiliation":[{"name":"Kyoto University and ATR, Kyoto, Japan"}]},{"given":"Takayuki","family":"Kanda","sequence":"additional","affiliation":[{"name":"Kyoto University and ATR, Kyoto, Japan"}]},{"given":"Hiroshi","family":"Ishiguro","sequence":"additional","affiliation":[{"name":"ATR, Japan and Osaka University, Japan"}]}],"member":"320","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2588880"},{"key":"e_1_2_1_2_1","unstructured":"P. Liu D. F. Glas T. Kanda and H. Ishiguro. 2017. Learning proactive behavior for interactive social robots. Auton. Rob. 1--19.  P. Liu D. F. Glas T. Kanda and H. Ishiguro. 2017. Learning proactive behavior for interactive social robots. Auton. Rob. 1--19."},{"key":"e_1_2_1_3_1","unstructured":"P. Liu D. F. Glas T. Kanda and H. Ishiguro. 2017. Two demonstrators are better than one\u2014A social robot that learns to imitate people with different interaction styles. IEEE Trans. Cog. Dev. Syst. 2017. Early access.  P. Liu D. F. Glas T. Kanda and H. Ishiguro. 2017. Two demonstrators are better than one\u2014A social robot that learns to imitate people with different interaction styles. IEEE Trans. Cog. Dev. Syst. 2017. Early access."},{"volume-title":"Proceedings of the RO-MAN\u201914","author":"Liu P.","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","unstructured":"C. Olah. 2015. Understanding LSTM Networks. Colah's blog. colah.github.io\/posts\/2015-08-Understanding-LSTMs\/.  C. Olah. 2015. Understanding LSTM Networks. Colah's blog. colah.github.io\/posts\/2015-08-Understanding-LSTMs\/."},{"volume-title":"Proceedings of the IAS\u201913","author":"Kuwamura K.","key":"e_1_2_1_6_1"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"K. Kuwamura S. Nishio and S. Sato. 2016. Can we talk through a robot as if face-to-face? Long-term fieldwork using teleoperated robot for seniors with Alzheimer's disease. Front. Psych. 7 (2016).  K. Kuwamura S. Nishio and S. Sato. 2016. Can we talk through a robot as if face-to-face? Long-term fieldwork using teleoperated robot for seniors with Alzheimer's disease. Front. Psych. 7 (2016).","DOI":"10.3389\/fpsyg.2016.01066"},{"volume-title":"Proceedings of the HRI\u201917","author":"Guo S.","key":"e_1_2_1_8_1"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1207\/s15327051hci1901&2_4"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2019.2895753"},{"volume-title":"Proceedings of the HRI\u201910","author":"P. H.","key":"e_1_2_1_11_1"},{"volume-title":"Proceedings of the RO-MAN\u201910","author":"Samani H. A.","key":"e_1_2_1_12_1"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3166054.3166058"},{"volume-title":"Proceedings of the AAAI\u201916","author":"Serban I. V.","key":"e_1_2_1_14_1"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/N15-1020"},{"volume-title":"Proceedings of the MA3HMI @ INTERSPEECH\u201914","author":"Kim Y.","key":"e_1_2_1_16_1"},{"volume-title":"Proceedings of the IROS\u201908","author":"Kidd C. D.","key":"e_1_2_1_17_1"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2005.1545303"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12369-013-0178-y"},{"volume-title":"Proceedings of the AAAI\u201907","author":"Nuxoll A. M.","key":"e_1_2_1_20_1"},{"volume-title":"Proceedings of the AGI\u201908","year":"2008","author":"Duch W.","key":"e_1_2_1_21_1"},{"volume-title":"Proceedings of the HRI\u201914","author":"Baxter P.","key":"e_1_2_1_22_1"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011191716506"},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","unstructured":"W. Wahlster and A. Kobsa. 1989. User models in dialog systems. In User Models in Dialog Systems. Springer 4--34.  W. Wahlster and A. Kobsa. 1989. User models in dialog systems. In User Models in Dialog Systems. Springer 4--34.","DOI":"10.1007\/978-3-642-83230-7_1"},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","unstructured":"A. Kobsa. 1990. User modeling in dialog systems: Potentials and hazards. AI 8 Soc. 4 3 (1990) 214--231  A. Kobsa. 1990. User modeling in dialog systems: Potentials and hazards. AI 8 Soc. 4 3 (1990) 214--231","DOI":"10.1007\/BF01889941"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364917690592"},{"key":"e_1_2_1_27_1","first-page":"00019","article-title":"A critical review of recurrent neural networks for sequence learning. Retrieved from","volume":"1506","author":"Lipton Z. C.","year":"2015","journal-title":"Arxiv Preprint Arxiv"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_1_29_1","first-page":"1078","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation. Retrieved from","volume":"1406","author":"Cho K.","year":"2014","journal-title":"Arxiv Preprint Arxiv"},{"volume-title":"Proceedings of the NIPS\u201915","author":"Sukhbaatar S.","key":"e_1_2_1_30_1"},{"volume-title":"Proceedings of the ICML\u201916","year":"2016","author":"Kumar A.","key":"e_1_2_1_31_1"},{"key":"e_1_2_1_32_1","first-page":"5401","article-title":"Neural turing machines. Retrieved from","volume":"1410","author":"Graves A.","year":"2014","journal-title":"Arxiv Preprint Arxiv"},{"volume-title":"Proceedings of the ICMI\u201914","author":"Admoni H.","key":"e_1_2_1_33_1"},{"volume-title":"Proceedings of the ICMI\u201916","author":"Leite I.","key":"e_1_2_1_34_1"},{"key":"e_1_2_1_35_1","first-page":"39","article-title":"The Restaurant Game: Learning social behavior and language from thousands of players online","volume":"3","author":"Orkin J.","year":"2007","journal-title":"J. Game Dev."},{"volume-title":"Proceedings of the AAMAS\u201909","author":"Orkin J.","key":"e_1_2_1_36_1"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.5898\/JHRI.2.1.Breazeal"},{"volume-title":"Proceedings of the RO-MAN\u201911","author":"Chernova S.","key":"e_1_2_1_38_1"},{"volume-title":"Proceedings of the AAAI Fall Symposium\u201910","author":"Chernova S.","key":"e_1_2_1_39_1"},{"key":"e_1_2_1_40_1","first-page":"2","volume-title":"Proceedings of the ACL\u201917 (Volume 2: Short Papers)","author":"Tian Z.","year":"2017"},{"volume-title":"Proceedings of the DSTC\u201917","author":"Perez J.","key":"e_1_2_1_41_1"},{"key":"e_1_2_1_42_1","first-page":"00057","article-title":"Frames: A corpus for adding memory to goal-oriented dialogue systems. Retrieved from","volume":"1704","author":"Asri L. E.","year":"2017","journal-title":"ArXiv Preprint Arxiv"},{"key":"e_1_2_1_43_1","first-page":"01690","article-title":"A frame tracking model for memory-enhanced dialogue systems. Retrieved from","volume":"1706","author":"Schulz H.","year":"2017","journal-title":"Arxiv Preprint Arxiv"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00203"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/THMS.2013.2283945"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm563"},{"key":"e_1_2_1_47_1","doi-asserted-by":"crossref","unstructured":"C. C. Aggarwal and C. Zhai. 2012. A survey of text clustering algorithms. In Mining Text Data. Springer 77--128.  C. C. Aggarwal and C. Zhai. 2012. A survey of text clustering algorithms. In Mining Text Data. Springer 77--128.","DOI":"10.1007\/978-1-4614-3223-4_4"},{"key":"e_1_2_1_48_1","first-page":"6980","article-title":"Adam: A method for stochastic optimization. Retrieved from","volume":"1412","author":"Kingma D. P.","year":"2014","journal-title":"ArXiv Preprint Arxiv"},{"key":"e_1_2_1_49_1","first-page":"08144","article-title":"Google's neural machine translation system: Bridging the gap between human and machine translation. Retrieved from","volume":"1609","author":"Wu Y.","year":"2016","journal-title":"Arxiv Preprint Arxiv"},{"volume-title":"Proceedings of the NIPS\u201917","year":"2017","author":"Vaswani A.","key":"e_1_2_1_50_1"},{"volume-title":"Proceedings of the ICRL\u201915","author":"Weston J.","key":"e_1_2_1_51_1"},{"key":"e_1_2_1_52_1","unstructured":"T. M. Cover and J. A. Thomas. 2012. Elements of Information Theory. John Wiley 8 Sons.  T. M. Cover and J. A. Thomas. 2012. Elements of Information Theory. John Wiley 8 Sons."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0087357"},{"volume-title":"Proceedings of the ICML\u201918","year":"2018","author":"Li Z.","key":"e_1_2_1_54_1"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3326462"}],"container-title":["ACM Transactions on Human-Robot Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3338810","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3338810","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:43:29Z","timestamp":1750207409000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3338810"}},"subtitle":["Learning a Memory Model of Human Behavior from Data"],"short-title":[],"issued":{"date-parts":[[2019,11,15]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,12,31]]}},"alternative-id":["10.1145\/3338810"],"URL":"https:\/\/doi.org\/10.1145\/3338810","relation":{},"ISSN":["2573-9522"],"issn-type":[{"type":"electronic","value":"2573-9522"}],"subject":[],"published":{"date-parts":[[2019,11,15]]},"assertion":[{"value":"2018-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-11-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}