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..
On a Scientific Discipline (Once) Named AI
Authors: Wolfgang Bibel
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The present article is therefore meant to provide just an analysis of the present state of affairs rather than pointing out a concrete plan for changing directions except for some indications in view of the given possibilities. |
| Researcher Affiliation | Academia | Wolfgang Bibel Darmstadt University of Technology EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | This is a theoretical paper and does not describe any new software or code implementation, therefore no open-source code is provided. |
| Open Datasets | No | This paper is theoretical and does not involve experimental training on datasets, thus no dataset access information is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments or their computational requirements, thus no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper and does not describe any software implementation or dependencies. |
| Experiment Setup | No | This paper is theoretical and does not describe any experimental setup or hyperparameters. |