Llm and Pddl

[TOC] Title: Pallagani Plansformer Generating Plans 2023 Author: Pallagani, Vishal et. al. Publish Year: GenPlan 2023 Workshop Review Date: Tue, Dec 24, 2024 url: https://arxiw.org/pdf/2212.08681 1 2 3 4 5 6 7 8 9 10 11 12 13 # input bibtex here @InProceedings{pallagani2023plansformer, author = {Pallagani, Vishal and Muppasani, Bharath and Murugesan, Keerthiram and Rossi, Francesca and Horesh, Lior and Srivastava, Biplav and Fabiano, Francesco and Loreggia, Andrea}, title = {Plansformer: Generating Symbolic Plans using Transformers}, booktitle = {Seventh Workshop on Generalization in Planning (GenPlan 2023)}, year = {2023}, month = {December}, address = {New Orleans, USA}, venue = {Room 238-239, New Orleans Ernest N. Morial Convention Center} } Pallagani, Vishal, et al. "Plansformer: Generating Symbolic Plans using Transformers." NeurIPS 2023 Workshop on Generalization in Planning. [!Note] ...

[TOC] Title: Dynamic Planning With a LLM Author: Gautier Dagan et. al. Publish Year: 11 Aug 2023 Review Date: Sun, Jan 21, 2024 url: arXiv:2308.06391v1 Summary of paper Motivation Traditional symbolic planners can find optimal solutions quickly but need complete and accurate problem representations. In contrast, LLMs can handle noisy data and uncertainty but struggle with planning tasks. The LLM-DP framework combines LLMs and traditional planners to solve embodied tasks efficiently. Traditional Planner need maximal information Some key terms Hallucination ...