Reliability In Planning and Learning the RIPL Workshop at ICAPS 2025

About the Workshop

RIPL is an evolution of the Reliable Data-Driven Planning and Scheduling (RDDPS) workshop that ran ICAPS 2022-2024. 

The workshop scope was edited to be more broadly inclusive, covering any aspect relevant to reliability in planning and learning, in particular LLM-generated planning models. The title was changed to reflect this scope in a more direct manner. RIPL will also explore connections with related workshops on Planning & Reinforcement Learning (PRL) and Language Models for Planning (LM4Plan).

Why it matters

Learning is the dominating trend in AI at this time. From a planning and scheduling perspective – and for sequential decision making in general – this is manifested in two major kinds of technical artifacts that are rapidly gaining importance. First, planning models generated by large language models, or otherwise learned or partially learned from data (such as a weather forecast in a model of flight actions). Second, planning/search information learned from data, in particular action policies or planning-control knowledge for making decisions in dynamic environments (reinforcement learning or per-domain generalizing knowledge in PDDL). Reliability is a key concern in such artefacts, prominently including safety, robustness, and fairness in various forms, but possibly other concerns as well. Arguably, this is indeed one of the grand challenges in AI for the foreseeable future.

Topics of Interest

The workshop welcomes contributions to any topic that roughly falls into the following problem space:

• Data-driven artifacts: Learned or ML-generated planning and scheduling models; Planning models, action strategies, and search guidance created through machine learning.
• Objectives: Reliability in whatever form, risk management, safety, robustness, fairness, scalability, and foundational design principles.
• Methodologies include any issue relating to robustness: methods for building, evaluating, validating, and working with these intelligent systems.
  
  

📣 Call for Contributions: RIPL Workshop @ ICAPS 2025

Key Deadlines:

Paper Submission: July 25, 2025 (UTC-12)

Author Notification: August 15, 2025

Camera-Ready Version: September 10, 2025

Organizing Committee

• Felipe Trevizan, Australian National University, Australia
• Charles Gretton, Australian National University, Australia
• Daniel Höller, Saarland University, Germany
• Marcel Steinmetz, University of Toulouse, France
• Marcel Vinzent, Saarland University, Germany
• Jörg Hoffmann, Saarland University, Germany
• Sylvie Thiebaux, University of Toulouse, France, and Australian National University, Australia

For all details please go to the RIPL page

https://icaps25.icaps-conference.org/program/workshops/ripl/