A Uniform Abstraction Framework for Generalized Planning
Authors: Zhenhe Cui, Yongmei Liu, Kailun Luo
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, by extending their abstraction framework, we propose a uniform abstraction framework for generalized planning. We formalize a generalized planning problem as a triple of a basic action theory, a trajectory constraint, and a goal. Then we define the concepts of sound abstractions of a generalized planning problem. We show that solutions to a generalized planning problem are nicely related to those of its sound abstractions. We also define and analyze the dual notion of complete abstractions. Finally, we review some important abstraction works for generalized planning and show that they can be formalized in our framework. |
| Researcher Affiliation | Academia | Zhenhe Cui1 , Yongmei Liu1 , Kailun Luo2 1Dept. of Computer Science, Sun Yat-sen University, Guangzhou 510006, China 2Dongguan University of Technology, Dongguan 523808, China |
| Pseudocode | No | The paper does not contain any pseudocode blocks or sections explicitly labeled as an algorithm. |
| Open Source Code | No | The paper does not include any statements about releasing open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using datasets. Therefore, no information on public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments using datasets. Therefore, no information on validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on formalizations within the situation calculus and Golog. It does not mention any specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not involve experiments. Therefore, no details about an experimental setup, such as hyperparameters or training settings, are provided. |