{"id":3459,"date":"2026-03-25T14:03:33","date_gmt":"2026-03-25T13:03:33","guid":{"rendered":"https:\/\/www.hsu-hh.de\/imb\/?page_id=3459"},"modified":"2026-07-10T11:54:08","modified_gmt":"2026-07-10T09:54:08","slug":"ijcai-ecai-2026-caipi-prl","status":"publish","type":"page","link":"https:\/\/www.hsu-hh.de\/imb\/en\/organized-events\/ijcai-ecai-2026-caipi-prl","title":{"rendered":"Joint Workshop on AI Planning for Complex Real-World Applications (CAIPI) and Bridging the Gap Between AI Planning and (Reinforcement) Learning (PRL)"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Aim and Scope<\/h1>\n\n\n\n

Symbolic planning, Reinforcement Learning, and emerging directions such as LLMs for planning and Neuro-Symbolic approaches all contribute important theoretical and applied perspectives on sequential decision-making. However, these communities often evolve in parallel, with distinct assumptions, theoretical backbones, methods, benchmarks, and forms of evaluation. As a result, progress on planning is fragmented across community boundaries, despite a shared interest in solving complex decision-making problems.<\/p>\n\n\n\n

This joint workshop aims to bring these communities together across both dimensions of the field: theoretical foundations and applications. It provides a platform for researchers working on symbolic, learning-based, and hybrid planning approaches to discuss common challenges, compare methodologies, and identify opportunities for integration. By fostering exchange across established and emerging research directions, the workshop seeks to strengthen connections within the broader planning community and support the development of more general and practically relevant planning approaches.<\/p>\n\n\n\n

Topics of Interest<\/h1>\n\n\n\n

We invite submissions at the intersection of AI Planning and (Reinforcement) Learning for theoretical and applied problems. The topics of interest include, but are not limited to, the following<\/p>\n\n\n\n