Epistemic Logic of Likelihood and Belief
Authors: James P. Delgrande, Joshua Sack, Gerhard Lakemeyer, Maurice Pagnucco
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a sound and complete proof system for the logic with respect to an underlying probabilistic semantics, and show that the language is equivalent to a sublanguage with no nested modalities. |
| Researcher Affiliation | Academia | School of Computing Science, Simon Fraser University, Burnaby, B.C., V5A 1S6 Canada. Dept. of Math. and Statistics, California State University Long Beach, CA 90840, USA. Dept. of Computer Science, RWTH Aachen University, D-52056 Aachen, Germany. School of Computer Science and Engineering, UNSW, Sydney, NSW 2052, Australia. |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code. |
| Open Datasets | No | The paper focuses on theoretical research and does not mention training or using datasets. |
| Dataset Splits | No | The paper focuses on theoretical research and does not mention validation splits for datasets. |
| Hardware Specification | No | The paper describes theoretical work and does not mention hardware specifications for running experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not provide details about an experimental setup or hyperparameters. |