Inductive Logic Programming: Challenges

Authors: Katsumi Inoue, Hayato Ohwada, Akihiro Yamamoto

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The paper is a summary and overview of the ILP 2015 conference, discussing topics, invited talks, and future challenges in the field of Inductive Logic Programming. While it references other research papers that conduct experiments and applications, it does not present its own empirical studies, data analysis, or experimental results. Its focus is on reviewing and summarizing the state of the art and future directions.
Researcher Affiliation Academia Katsumi Inoue National Institute of Informatics Tokyo, Japan inoue@nii.ac.jp; Hayato Ohwada Tokyo University of Science Noda, Japan ohwada@rs.tus.ac.jp; Akihiro Yamamoto Kyoto University Kyoto, Japan akihiro@i.kyoto-u.ac.jp
Pseudocode No The paper is an overview of a conference and does not contain any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper states: 'All papers and slides presented in technical sessions have been made open at the website.1 http://www.ilp2015.jp/'. This refers to the conference proceedings and presentations, not open-source code directly linked to the content of this summary paper itself, nor does it refer to the authors' own code for the paper's specific content.
Open Datasets No The paper is a survey of the ILP field and its recent conference. It discusses various applications and research topics from other papers but does not use or provide access information for a dataset used for training by the authors of this paper.
Dataset Splits No The paper is a conference overview and does not describe any experiments conducted by its authors. Therefore, it does not provide details on training/test/validation dataset splits.
Hardware Specification No The paper is a survey and does not report on specific hardware used for experiments.
Software Dependencies No The paper is a survey and does not provide specific software dependencies with version numbers used for its own work.
Experiment Setup No The paper is a survey of the ILP field and its recent conference. It does not describe an experimental setup, hyperparameters, or system-level training settings for its own work.