{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:30:37Z","timestamp":1743024637567,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031334689"},{"type":"electronic","value":"9783031334696"}],"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\/978-3-031-33469-6_28","type":"book-chapter","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T21:02:01Z","timestamp":1684875721000},"page":"276-285","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can Language Models Be Used in Multistep Commonsense Planning Domains?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7347-3701","authenticated-orcid":false,"given":"Zhisheng","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5988-8305","authenticated-orcid":false,"given":"Mayank","family":"Kejriwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"unstructured":"Hugging face repository: bert-base-uncased. https:\/\/huggingface.co\/bert-base-uncased","key":"28_CR1"},{"key":"28_CR2","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"28_CR3","first-page":"15084","volume":"34","author":"L Chen","year":"2021","unstructured":"Chen, L., et al.: Decision transformer: reinforcement learning via sequence modeling. Adv. Neural Inf. Process. Syst. 34, 15084\u201315097 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"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)","key":"28_CR4"},{"doi-asserted-by":"crossref","unstructured":"Hausknecht, M., Ammanabrolu, P., C\u00f4t\u00e9, M.A., Yuan, X.: Interactive fiction games: a colossal adventure. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 7903\u20137910 (2020)","key":"28_CR5","DOI":"10.1609\/aaai.v34i05.6297"},{"doi-asserted-by":"crossref","unstructured":"Murugesan, K., et al.: Text-based rl agents with commonsense knowledge: new challenges, environments and baselines. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9018\u20139027 (2021)","key":"28_CR6","DOI":"10.1609\/aaai.v35i10.17090"},{"unstructured":"Murugesan, K., Atzeni, M., Shukla, P., Sachan, M., Kapanipathi, P., Talamadupula, K.: Enhancing text-based reinforcement learning agents with commonsense knowledge. arXiv preprint arXiv:2005.00811 (2020)","key":"28_CR7"},{"issue":"3","key":"28_CR8","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.221585","volume":"10","author":"Z Tang","year":"2023","unstructured":"Tang, Z., Kejriwal, M.: Can language representation models think in bets? R. Soc. Open Sci. 10(3), 221585 (2023)","journal-title":"R. Soc. Open Sci."},{"issue":"6","key":"28_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3530811","volume":"55","author":"Y Tay","year":"2022","unstructured":"Tay, Y., Dehghani, M., Bahri, D., Metzler, D.: Efficient transformers: a survey. ACM Comput. Surv. 55(6), 1\u201328 (2022)","journal-title":"ACM Comput. Surv."},{"doi-asserted-by":"crossref","unstructured":"Wang, R., Jansen, P., C\u00f4t\u00e9, M.A., Ammanabrolu, P.: Scienceworld: Is your agent smarter than a 5th grader? arXiv preprint arXiv:2203.07540 (2022)","key":"28_CR10","DOI":"10.18653\/v1\/2022.emnlp-main.775"},{"doi-asserted-by":"publisher","unstructured":"Yao, S., Rao, R., Hausknecht, M., Narasimhan, K.: Keep CALM and explore: language models for action generation in text-based games. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8736\u20138754. Association for Computational Linguistics, Online, November 2020. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.704, https:\/\/aclanthology.org\/2020.emnlp-main.704","key":"28_CR11","DOI":"10.18653\/v1\/2020.emnlp-main.704"}],"container-title":["Lecture Notes in Computer Science","Artificial General Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33469-6_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T21:00:33Z","timestamp":1702501233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33469-6_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031334689","9783031334696"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33469-6_28","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":"24 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial General Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stockholm","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","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":"16 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"agi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/agi-conf.org\/2023\/","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":"72","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":"35","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":"1","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":"49% - 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":"2.2","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":"2.9","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)"}}]}}