3 papers
cs.AI2026
Symmetry-Aware Transformer Training for Automated Planning
Markus Fritzsche, Elliot Gestrin, Jendrik Seipp
While transformers excel in many settings, their application in the field of automated planning is limited. Prior work like PlanGPT, a state-of-the-art decoder-only transformer, st…
cs.AI2026
LLM-Evolved Domain-Independent Heuristics for Symbolic AI Planning
Elliot Gestrin, Jendrik Seipp
Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers. Recent work has shown tha…
cs.AI2025
NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions
Elliot Gestrin, Marco Kuhlmann, Jendrik Seipp
Classical planners are powerful systems, but modeling tasks in input formats such as PDDL is tedious and error-prone. In contrast, planning with Large Language Models (LLMs) allows…