Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On the Completeness of Best-First Search Variants That Use Random Exploration
Authors: Richard Valenzano, Fan Xie
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide a theoretical justification for this increased robustness by formally analyzing how these algorithms behave on infinite graphs. |
| Researcher Affiliation | Academia | Richard Valenzano University of Toronto Toronto, Canada EMAIL Fan Xie University of Alberta Edmonton, Canada EMAIL |
| Pseudocode | Yes | Algorithm 1 The OCL Algorithm Framework |
| Open Source Code | No | The paper is theoretical and focuses on formal analysis; it does not mention or provide access to any open-source code for an implementation of its concepts. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not discuss experimental validation using dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |