Model Checking Probabilistic Knowledge: A PSPACE Case
Authors: Xiaowei Huang, Marta Kwiatkowska
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Model checking probabilistic knowledge of memoryful semantics is undecidable... In this paper, we propose to work with an additional restriction... A PSPACE-complete case is identified... We will be interested in the problem of model checking formulas in this system... The model checking problem is then to determine... We present the proof idea only. |
| Researcher Affiliation | Academia | Xiaowei Huang and Marta Kwiatkowska University of Oxford, UK |
| Pseudocode | No | The paper includes a section titled "Algorithm" but describes the process in narrative text rather than structured pseudocode or an algorithm block with formal steps and syntax. It states, "Most details are omitted." |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | This is a theoretical paper discussing model checking and logic complexity. It does not conduct empirical experiments using datasets in the traditional sense, thus no public dataset is mentioned or provided. |
| Dataset Splits | No | This is a theoretical paper focusing on logical decidability and complexity, not empirical evaluation. Therefore, there are no dataset splits for training, validation, or testing mentioned. |
| Hardware Specification | No | This is a theoretical paper focusing on model checking and logic. It does not conduct empirical experiments, so no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | This is a theoretical paper. It does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical paper focusing on model checking and logic. It does not describe an experimental setup with hyperparameters or specific system-level training settings. |