{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:16:06Z","timestamp":1743156966659,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031789540"},{"type":"electronic","value":"9783031789557"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78955-7_21","type":"book-chapter","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T14:19:23Z","timestamp":1737987563000},"page":"256-273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Static and\u00a0Adaptive Planning with\u00a0WoT TD by\u00a0Generating Python Objects as\u00a0Intermediary Representations Using Large Language Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8275-1284","authenticated-orcid":false,"given":"Lukas","family":"Kinder","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-7457","authenticated-orcid":false,"given":"Tobias","family":"K\u00e4fer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"21_CR1","unstructured":"Aeronautiques, C., et al.: Pddl| the planning domain definition language. Tech. Rep, Technical Report (1998)"},{"key":"21_CR2","unstructured":"Ahn, M., et\u00a0al.: Do as i can, not as i say: grounding language in robotic affordances. arXiv preprint arXiv:2204.01691 (2022)"},{"issue":"5\u20136","key":"21_CR3","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.artint.2008.11.011","volume":"173","author":"JA Baier","year":"2009","unstructured":"Baier, J.A., Bacchus, F., McIlraith, S.A.: A heuristic search approach to planning with temporally extended preferences. Artif. Intell. 173(5\u20136), 593\u2013618 (2009)","journal-title":"Artif. Intell."},{"key":"21_CR4","unstructured":"Bubeck, S., et\u00a0al.: Sparks of artificial general intelligence: early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023)"},{"key":"21_CR5","unstructured":"Chen, W., Ma, X., Wang, X., Cohen, W.W.: Program of thoughts prompting: disentangling computation from reasoning for numerical reasoning tasks. arXiv preprint arXiv:2211.12588 (2022)"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Denny, P., Kumar, V., Giacaman, N.: Conversing with copilot: exploring prompt engineering for solving cs1 problems using natural language. In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education, vol. 1, pp. 1136\u20131142 (2023)","DOI":"10.1145\/3545945.3569823"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Ferguson, D., Stentz, A.: Anytime, dynamic planning in high-dimensional search spaces. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 1310\u20131315. IEEE (2007)","DOI":"10.1109\/ROBOT.2007.363166"},{"issue":"3\u20134","key":"21_CR8","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/0004-3702(71)90010-5","volume":"2","author":"RE Fikes","year":"1971","unstructured":"Fikes, R.E., Nilsson, N.J.: Strips: A new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3\u20134), 189\u2013208 (1971)","journal-title":"Artif. Intell."},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Fox, M., Long, D.: Pddl2. 1: an extension to pddl for expressing temporal planning domains. J. Artif. Intell. Res. 20, 61\u2013124 (2003)","DOI":"10.1613\/jair.1129"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Garrett, C.R., Lozano-P\u00e9rez, T., Kaelbling, L.P.: Pddlstream: integrating symbolic planners and blackbox samplers via optimistic adaptive planning. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol.\u00a030, pp. 440\u2013448 (2020)","DOI":"10.1609\/icaps.v30i1.6739"},{"key":"21_CR11","unstructured":"Hou, X., et al.: Large language models for software engineering: a systematic literature review. arXiv preprint arXiv:2308.10620 (2023)"},{"key":"21_CR12","unstructured":"Huang, W., Abbeel, P., Pathak, D., Mordatch, I.: Language models as zero-shot planners: extracting actionable knowledge for embodied agents. In: International Conference on Machine Learning, pp. 9118\u20139147. PMLR (2022)"},{"key":"21_CR13","unstructured":"Huang, W., et\u00a0al.: Inner monologue: embodied reasoning through planning with language models. arXiv preprint arXiv:2207.05608 (2022)"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Jansen, P.A.: Visually-grounded planning without vision: language models infer detailed plans from high-level instructions. arXiv preprint arXiv:2009.14259 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.395"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Jiang, Y.Q., Zhang, S.Q., Khandelwal, P., Stone, P.: Task planning in robotics: an empirical comparison of pddl-and asp-based systems. Front. Inf. Technol. Electron. Eng. 20, 363\u2013373 (2019)","DOI":"10.1631\/FITEE.1800514"},{"key":"21_CR16","unstructured":"Kaebisch, S., Kamiya, T., McCool, M., Charpenay, V., Korkan, E., Kovatsch, M.: Web of things (wot) thing description 1.1. W3C recommendation, W3C (2020). https:\/\/www.w3.org\/TR\/wot-thing-description11\/"},{"key":"21_CR17","unstructured":"Kinder, L., K\u00e4fer, T.: Towards improving large language models\u2019 planning capabilities on wot thing descriptions by generating python objects as intermediary representations. In: First International Workshop on Actionable Knowledge Representation and Reasoning for Robots (2024)"},{"key":"21_CR18","unstructured":"Kis, Z., Peintner, D., Aguzzi, C., Hund, J., Nimura, K.: Web of Things (WOT) scripting API. W3C Recommendation W3C (2023). https:\/\/www.w3.org\/TR\/wot-scripting-api\/"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Koenig, S., Likhachev, M.: Improved fast replanning for robot navigation in unknown terrain. In: Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), vol.\u00a01, pp. 968\u2013975. IEEE (2002)","DOI":"10.1109\/ROBOT.2002.1013481"},{"key":"21_CR20","unstructured":"Li, C., et al.: Chain of code: reasoning with a language model-augmented code emulator. arXiv preprint arXiv:2312.04474 (2023)"},{"key":"21_CR21","unstructured":"Li, C., et\u00a0al.: Behavior-1k: a benchmark for embodied AI with 1,000 everyday activities and realistic simulation. In: Conference on Robot Learning, pp. 80\u201393. PMLR (2023)"},{"key":"21_CR22","unstructured":"Li, S., et al.: Pre-trained language models for interactive decision-making. Adv. Neural. Inf. Process. Syst. 35, 31199\u201331212 (2022)"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Liang, J., et al.: Code as policies: language model programs for embodied control. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 9493\u20139500. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10160591"},{"key":"21_CR24","unstructured":"Liu, B., et al.: Llm+ p: empowering large language models with optimal planning proficiency. arXiv preprint arXiv:2304.11477 (2023)"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Madaan, A., Zhou, S., Alon, U., Yang, Y., Neubig, G.: Language models of code are few-shot commonsense learners. arXiv preprint arXiv:2210.07128 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.90"},{"key":"21_CR26","unstructured":"Nau, D., Cao, Y., Lotem, A., Munoz-Avila, H.: Shop: simple hierarchical ordered planner. In: Proceedings of the 16th International Joint Conference on Artificial intelligence, vol. 2. pp. 968\u2013973 (1999)"},{"key":"21_CR27","unstructured":"Nilsson, N.J., et\u00a0al.: Shakey the robot. Technical Note 323, SRI A.I. Center (1984)"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Pan, L., Albalak, A., Wang, X., Wang, W.Y.: Logic-LM: empowering large language models with symbolic solvers for faithful logical reasoning. arXiv preprint arXiv:2305.12295 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.248"},{"key":"21_CR29","unstructured":"Parcalabescu, L., Gatt, A., Frank, A., Calixto, I.: Seeing past words: testing the cross-modal capabilities of pretrained v &l models on counting tasks. arXiv preprint arXiv:2012.12352 (2020)"},{"key":"21_CR30","unstructured":"Roziere, B., et\u00a0al.: Code llama: open foundation models for code. arXiv preprint arXiv:2308.12950 (2023)"},{"key":"21_CR31","unstructured":"Schick, T., et al.: Toolformer: language models can teach themselves to use tools. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Shridhar, M., et al.: Alfred: a benchmark for interpreting grounded instructions for everyday tasks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10740\u201310749 (2020)","DOI":"10.1109\/CVPR42600.2020.01075"},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Singh, I., et al.: Progprompt: generating situated robot task plans using large language models. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 11523\u201311530. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10161317"},{"key":"21_CR34","unstructured":"Skreta, M., Zhou, Z., Yuan, J.L., Darvish, K., Aspuru-Guzik, A., Garg, A.: Replan: robotic replanning with perception and language models. arXiv preprint arXiv:2401.04157 (2024)"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Song, C.H., Wu, J., Washington, C., Sadler, B.M., Chao, W.L., Su, Y.: Llm-planner: few-shot grounded planning for embodied agents with large language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2998\u20133009 (2023)","DOI":"10.1109\/ICCV51070.2023.00280"},{"key":"21_CR36","unstructured":"Wang, X., et al.: Executable code actions elicit better LLM agents. arXiv preprint arXiv:2402.01030 (2024)"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, S., Ji, H.: Code4struct: code generation for few-shot event structure prediction. arXiv preprint arXiv:2210.12810 (2022)","DOI":"10.18653\/v1\/2023.acl-long.202"},{"key":"21_CR38","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824\u201324837 (2022)"},{"key":"21_CR39","unstructured":"Zeng, A., et\u00a0al.: Socratic models: composing zero-shot multimodal reasoning with language. arXiv preprint arXiv:2204.00598 (2022)"},{"key":"21_CR40","unstructured":"Zhang, H., et al.: Building cooperative embodied agents modularly with large language models. arXiv preprint arXiv:2307.02485 (2023)"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web: ESWC 2024 Satellite Events"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78955-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T14:19:43Z","timestamp":1737987583000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78955-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031789540","9783031789557"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78955-7_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"28 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esws2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.eswc-conferences.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}