Welcome to Language-to-Plan (L2P)! ================================== .. toctree:: :maxdepth: 2 :titlesonly: :caption: Contents: getting_started l2p paper_recreations templates types paper_feed What is L2P? ------------ This library is a collection of tools for PDDL models extracted from natural language driven by large language models. This library is an expansion from the survey paper `"LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models" `_. L2P is an offline, NL to PDDL system that supports domain-agnostic planning. It does this via creating an intermediate PDDL representation of the domain and task, which can then be solved by a classical planner. Our GitHub can be found `here `_. L2P PyPI can be found `here `_. Installation ------------ Install ``l2p`` by running:: pip install l2p Usage ----- :doc:`getting_started` is the place to go to hit the ground running on using l2p. The :doc:`l2p` documentation provides in-depth codeu usage for the library. Support ------- If you are having issues, please let us know. Reach out to us at 20mt1@queensu.ca or by creating a GitHub issue. License ------- The project is licensed under the MIT license for the Queen's Mu Lab. About L2P --------- With the proliferation of related techniques to convert NL to PDDL, we are seeing an ever-increasing set of related methods. To bring them together under a single computational umbrella, and beyond just relating the work together conceptually as we have done thus far in this survey, we created a unified framework that encompasses the vast majority of existing methods: Language-to-Plan (L2P). This Python library is open source and captures a generalised version of the proposed `"LLM-Modulo" framework `_ , which emphasizes soundness guarantees through iterative plan refining via external verifiers. While L2P embodies the core principles of the LLM-Modulo framework, which advocates for LLMs autonomously generating plan candidates themselves, it shifts the focus by facilitating the creation of PDDL files using LLMs – aligning with this paper's paradigm. This approach allows for the integration of external verifiers and feedback critics, enabling users to modify and refine the extracted models.