Implicitly learning to reason in first-order logic
Authors: Vaishak Belle, Brendan Juba
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we present a new theoretical approach to robustly learning to reason in first-order logic, and consider universally quantified clauses over a countably infinite domain. |
| Researcher Affiliation | Academia | Vaishak Belle University of Edinburgh & Alan Turing Institute vaishak@ed.ac.uk Brendan Juba Washington University in St. Louis bjuba@wustl.edu |
| Pseudocode | Yes | Algorithm 1 Reasoning with implicit learning |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical framework and does not perform experiments with datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper presents a theoretical framework and does not conduct experiments, therefore it does not provide dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper presents a theoretical framework and does not describe any experimental setup or hyperparameters. |