Metaphysics of Planning Domain Descriptions
Authors: Siddharth Srivastava, Stuart Russell, Alessandro Pinto
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We presented an analysis of representational abstractions for planning problem speciļ¬cations and proved several results categorizing abstraction mechanisms that exhibit desirable properties such as the Markov property. |
| Researcher Affiliation | Collaboration | Siddharth Srivastava1 and Stuart Russell2 and Alessandro Pinto1 1 United Technologies Research Center, Berkeley, CA 94705 2 Computer Science Division, University of California, Berkeley CA 94720 |
| Pseudocode | No | The paper includes definitions, theorems, and model specifications, but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper uses the 'blocks-world model' as an illustrative example, but it does not specify or provide access information for any publicly available or open dataset used for empirical evaluation. |
| Dataset Splits | No | As the paper is theoretical and does not conduct empirical studies, it does not provide training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details like hyperparameters or system-level training settings. |