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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The Complexity of Limited Belief Reasoning—The Quantifier-Free Case
Authors: Yijia Chen, Abdallah Saffidine, Christoph Schwering
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper investigates the computational complexity of reasoning with belief levels. |
| Researcher Affiliation | Academia | 1 Fudan University, Shanghai 201203, China 2 Australian National University, Canberra ACT 2600, Australia 3 University of New South Wales, Sydney NSW 2052, Australia |
| Pseudocode | No | The paper describes a decision procedure textually but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve datasets or training, so there is no information about publicly available training data. |
| Dataset Splits | No | The paper is theoretical and does not involve datasets, so there is no information about training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for experiments, as it is a theoretical paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |