Chain Length and CSPs Learnable with Few Queries
Authors: Christian Bessiere, Clément Carbonnel, George Katsirelos1420-1427
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The entire paper is a theoretical analysis with proofs and lemmas, without any experimental setup, data, or results section. For example, Section 3 describes "Constraint acquisition and chain length" with a Theorem 7 proof for learnability without any empirical validation. |
| Researcher Affiliation | Academia | 1LIRMM, CNRS, University of Montpellier, France {christian.bessiere, clement.carbonnel}@lirmm.fr 2UMR MIA-Paris, INRA, Agro Paris Tech, Universit e Paris-Saclay, Paris, France gkatsi@gmail.com |
| Pseudocode | No | The paper describes an algorithm verbally in the proof of Theorem 7, but it does not contain a structured pseudocode block or a clearly labeled algorithm figure. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention the use of any datasets, public or otherwise, for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not include any details about an experimental setup or hyperparameters. |