{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T11:40:21Z","timestamp":1730461221136,"version":"3.28.0"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466601"},{"type":"electronic","value":"9783031466618"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46661-8_38","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"569-583","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Reinforcement Learning for\u00a0Solving the\u00a0Trip Planning Query"],"prefix":"10.1007","author":[{"given":"Changlin","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Ying","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jiajia","family":"Li","sequence":"additional","affiliation":[]},{"given":"Na","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Qiu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"38_CR1","doi-asserted-by":"crossref","unstructured":"Li, Y. Cheng, D., Marios, H., George, K., Teng, S.: On trip planning queries in spatial databases. In: SSTD, pp. 273\u2013290 (2005)","DOI":"10.1007\/11535331_16"},{"key":"38_CR2","doi-asserted-by":"crossref","unstructured":"Tanzima, H., Tahrima, H., Mohammed A., Lars, K.: Group trip planning queries in spatial databases. In: SSTD, pp. 259\u2013276 (2013)","DOI":"10.1007\/978-3-642-40235-7_15"},{"issue":"4","key":"38_CR3","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/s00778-006-0038-6","volume":"17","author":"S Mehdi","year":"2008","unstructured":"Mehdi, S., Mohammad, R., Kolahdouzan, C.S.: The optimal sequenced route query. VLDB J. 17(4), 765\u2013787 (2008)","journal-title":"VLDB J."},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Ouyang, D., Qin, L., Chang, L., Lin, X., Zhang, Y., Zhu, Q.: When hierarchy meets 2-Hop-labeling: efficient shortest distance queries on road networks. In: SIGMOD, pp. 709\u2013724 (2018)","DOI":"10.1145\/3183713.3196913"},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Anasthasia, A., Md. Saiful, I., David, T., Muhammad, A.:IG-Tree: an efficient spatial keyword index for planning best path queries on road networks. In: WWW, vol. 22(4), pp. 1359\u20131399 (2019)","DOI":"10.1007\/s11280-018-0643-5"},{"key":"38_CR6","unstructured":"Nina, M., Sergei, S., Sergei, I. Evgeny, B.: Reinforcement learning for combinatorial optimization: A Survey. CoRR abs\/ arXiv: 2003.03600 (2020)"},{"key":"38_CR7","unstructured":"Applegate, L., Bixby, E., Vaek, C., Cook, J.: The traveling salesman problem: a computational study. In: PUP (2006)"},{"key":"38_CR8","doi-asserted-by":"crossref","unstructured":"Hopfield, J.: Neural computation of decisions in optimization problems. Biol. Cybern. 52(1985)","DOI":"10.1007\/BF00339943"},{"key":"38_CR9","unstructured":"Petar, V., Guillem, C., Arantxa, C., Adriana, R., Pietro, L., Yoshua B.: Graph attention networks. In: ICLR (Poster) (2018)"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Iddo, D.A., et al.: Learning to solve combinatorial optimization problems on real-world graphs in linear time. In: ICMLA, pp. 19\u201324 (2020)","DOI":"10.1109\/ICMLA51294.2020.00013"},{"key":"38_CR11","unstructured":"Oriol, V., Meire, F., Navdeep, J.: Pointer networks. In: NIPS, pp. 2692\u20132700 (2015)"},{"key":"38_CR12","unstructured":"Ashish, V., et al: Attention is all you need. In: NIPS, pp. 5998\u20136008 (2017)"},{"key":"38_CR13","unstructured":"Yoav, K., Lior, W.: Learning the Multiple Traveling Salesmen Problem with Permutation Invariant Pooling Networks. CoRR abs\/ arXiv: 1803.09621 (2018)"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Michel, D., Pierre, C., Alexandre, L., Yossiri, A., Louis-Martin, R.: Learning heuristics for the TSP by policy gradient. In: CPAIOR, pp. 170\u2013181 (2018)","DOI":"10.1007\/978-3-319-93031-2_12"},{"key":"38_CR15","unstructured":"Wouter, K., Herke van, H., Max, W.: Attention, learn to solve routing problems! In: ICLR (Poster) (2019)"},{"key":"38_CR16","unstructured":"Xavier, B., Thomas, L.: The Transformer Network for the Traveling Salesman Problem. CoRR abs\/ arXiv: 2103.03012 (2021)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46661-8_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T11:08:04Z","timestamp":1730459284000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46661-8_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466601","9783031466618"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46661-8_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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)"}}]}}