A Semantical Analysis of Second-Order Propositional Modal Logic
Authors: Francesco Belardinelli, Wiebe van der Hoek
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is aimed as a contribution to the use of formal modal languages in Artificial Intelligence. We introduce a multi-modal version of Second-order Propositional Modal Logic (SOPML), an extension of modal logic with propositional quantification, and illustrate its usefulness as a specification language for knowledge representation as well as temporal and spatial reasoning. Then, we define novel notions of (bi)simulation and prove that these preserve the interpretation of SOPML formulas. Finally, we apply these results to assess the expressive power of SOPML. |
| Researcher Affiliation | Academia | F. Belardinelli Laboratoire IBISC Universit e d Evry, France belardinelli@ibisc.fr W. van der Hoek University of Liverpool, UK wiebe@liverpool.ac.uk |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers (e.g., Python, PyTorch versions) for its analysis. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |