A complementary study on PlanGPT: Evaluation with defined Performance Metrics and comparison with a planner
Automated Planning is a subfield of Artificial Intelligence (AI) where the main objective is generating a sequence of actions, known as a plan, that helps us reach a goal state from an initial state. A planning problem is defined by a set of objects, an initial state and a desired goal state. The objective is to compute a plan that'll lead us from the inital state to the goal state. Programs that generate plans are called planners. In this paper, we did a complementary study to the state-of-the-art LLM called PlanGPT which was released last year. We redid some experiments to verify whether planning with LLMs is \textbf{pertinent} and \textbf{worthwhile}. We also check whether the results ...
| Authors | Youssef Abdelkader, Humbert Fiorino, Damien Pellier |
|---|---|
| Primary category | cs.AI |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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