General Policies, Representations, and Planning Width
Authors: Blai Bonet, Hector Geffner11764-11773
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A longer version of this paper with all the proofs is available (Bonet and Geffner 2020a).Our setting is slightly different as our numerical features are linear and cannot grow without bound as in QNPs, but we do not use this property to define termination.Table 2: Summary of main formal results. |
| Researcher Affiliation | Academia | Blai Bonet,1 Hector Geffner1,2 1 Universitat Pompeu Fabra, Barcelona, Spain 2Instituci o Catalana de Recerca i Estudis Avanc ats (ICREA) |
| Pseudocode | No | The paper describes algorithms such as IW, SIWΦ, and SIWR in text, but does not provide them in structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper discusses classical planning domains such as Blocksworld, Boxes, and Delivery as conceptual examples, but does not specify any actual datasets used for training or provide access information for them. |
| Dataset Splits | No | The paper does not describe empirical experiments or provide details on dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical in nature and does not describe empirical experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not detail empirical implementations, therefore specific software dependencies with version numbers are not listed. |
| Experiment Setup | No | The paper focuses on theoretical concepts and does not report empirical experiments, therefore no specific experimental setup details or hyperparameters are provided. |