5 papers
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…
Frontier Large Language Models Rival State-of-the-Art Planners
Augusto B. Corrêa, André G. Pereira, Jendrik Seipp
A series of influential studies established that large language models cannot reliably solve even simple planning tasks. We show that the latest generation of frontier models overt…
Reinforcement Learning with Reward Machines for Sleep Control in Mobile Networks
Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis +2
Energy efficiency in mobile networks is crucial for sustainable telecommunications infrastructure, particularly as network densification continues to increase power consumption. Sl…
Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code
Augusto B. Corrêa, André G. Pereira, Jendrik Seipp
In recent years, large language models (LLMs) have shown remarkable capabilities in various artificial intelligence problems. However, they fail to plan reliably, even when prompte…
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…