{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T03:15:36Z","timestamp":1789096536087,"version":"build-2803163510"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,8,17]],"date-time":"2025-08-17T00:00:00Z","timestamp":1755388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,17]],"date-time":"2025-08-17T00:00:00Z","timestamp":1755388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,17]]},"DOI":"10.1109\/case58245.2025.11164110","type":"proceedings-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:24:07Z","timestamp":1758648247000},"page":"1701-1708","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning"],"prefix":"10.1109","author":[{"given":"Harisankar","family":"Babu","sequence":"first","affiliation":[{"name":"Bosch Center for Artificial Intelligence,Renningen,Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Schillinger","sequence":"additional","affiliation":[{"name":"Bosch Center for Artificial Intelligence,Renningen,Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamim","family":"Asfour","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology,Karlsruhe,Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref2","first-page":"22199","article-title":"Large language models are zero-shot reasoners","volume":"35","author":"Kojima","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref3","first-page":"11809","article-title":"Tree of thoughts: Deliberate problem solving with large language models","volume":"36","author":"Yao","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i16.29720"},{"key":"ref5","first-page":"9118","article-title":"Language models as zero-shot planners: Extracting actionable knowledge for embodied agents","volume-title":"Proc. of the 39th Int. Conf. on Machine Learning","volume":"162","author":"Huang"},{"key":"ref6","first-page":"287","article-title":"Do as i can, not as i say: Grounding language in robotic affordances","volume-title":"Proc. of The 6th Conf. on Robot Learning","volume":"205","author":"Ichter"},{"key":"ref7","first-page":"1769","article-title":"Inner monologue: Embodied reasoning through planning with language models","volume-title":"Proc. of The 6th Conf. on Robot Learning","volume":"205","author":"Huang"},{"key":"ref8","first-page":"8634","article-title":"Reflexion: language agents with verbal reinforcement learning","volume":"36","author":"Shinn","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref9","article-title":"Critic: Large language models can self-correct with tool-interactive critiquing","author":"Gou","year":"2023"},{"key":"ref10","article-title":"React: Synergizing reasoning and acting in language models","author":"Yao","year":"2022"},{"key":"ref11","first-page":"23","article-title":"Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning","volume-title":"Proc. of The 7th Conf. on Robot Learning","volume":"229","author":"Rana"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.507"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i18.30006"},{"key":"ref14","first-page":"38975","article-title":"Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change","volume":"36","author":"Valmeekam","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref15","article-title":"Llms can\u2019t plan, but can help planning in llm-modulo frameworks","volume-title":"Forty-first Int. Conf. on Machine Learning","author":"Kambhampati"},{"key":"ref16","article-title":"Llm+ p: Empowering large language models with optimal planning proficiency","author":"Liu","year":"2023"},{"key":"ref17","article-title":"Dynamic planning with a llm","author":"Dagan","year":"2023"},{"key":"ref18","first-page":"79081","article-title":"Leveraging pre-trained large language models to construct and utilize world models for model-based task planning","volume":"36","author":"Guan","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref19","article-title":"Nl2plan: Robust llm-driven planning from minimal text descriptions","author":"Gestrin","year":"2024"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/icra55743.2025.11127951"},{"key":"ref21","article-title":"Clmasp: Coupling large language models with answer set programming for robotic task planning","author":"Lin","year":"2024"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.321"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA57147.2024.10611163"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2024.XX.112"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/icra55743.2025.11127838"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/IROS58592.2024.10801986"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/Humanoids58906.2024.10769803"},{"key":"ref28","article-title":"Openvla: An open-source vision-language-action model","author":"Kim","year":"2024"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/icra57147.2024.10611477"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00886"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.432"}],"event":{"name":"2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)","location":"Los Angeles, CA, USA","start":{"date-parts":[[2025,8,17]]},"end":{"date-parts":[[2025,8,21]]}},"container-title":["2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11163731\/11163732\/11164110.pdf?arnumber=11164110","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T13:08:17Z","timestamp":1759237697000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11164110\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,17]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/case58245.2025.11164110","relation":{},"subject":[],"published":{"date-parts":[[2025,8,17]]}}}