The Hardness of Reasoning about Probabilities and Causality
Authors: Benito van der Zander, Markus Bläser, Maciej Liśkiewicz
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution of this work is establishing the exact computational complexity of these satisfiability problems. We introduce a new natural complexity class, named succ R, which can be viewed as a succinct variant of the well-studied class R, and show that these problems are complete for succ R. |
| Researcher Affiliation | Academia | 1Institute of Theoretical Computer Science, University of L ubeck, Germany 2Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention any open-source code for the described methodology. |
| Open Datasets | No | The paper describes theoretical work and does not mention the use of any datasets for training. |
| Dataset Splits | No | The paper describes theoretical work and does not mention the use of any datasets for validation. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not mention any specific software dependencies or versions for experimental setup. |
| Experiment Setup | No | The paper describes theoretical work and does not include details about an experimental setup, such as hyperparameters or training settings. |