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..
Second-Order Quantified Boolean Logic
Authors: Jie-Hong R. Jiang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate the second-order quantified Boolean logic with the following main results: First, we present a procedure of quantifier elimination converting SOQBFs to QBFs and a game interpretation of SOQBF semantics. Second, we devise a sound and complete refutation-proof system for SOQBF. Third, we develop an algorithm for countermodel extraction from a refutation proof. Finally, we show potential applications of SOQBFs in system design and multi-agent planning. |
| Researcher Affiliation | Academia | Jie-Hong R. Jiang Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan EMAIL |
| Pseudocode | Yes | Algorithm 1: Countermodel Extraction |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets for training, validation, or testing. The examples provided (Example 1, Example 2) are illustrative within the theoretical framework. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies. Therefore, it does not specify dataset splits for validation or other purposes. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments or implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments or their setup, hyperparameters, or training configurations. |