Probabilistic Alternating-Timeµ-Calculus
Authors: Fu Song, Yedi Zhang, Taolue Chen, Yu Tang, Zhiwu Xu6179-6186
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper focuses on proposing a new logic (PAMC), defining its syntax and semantics, proving its properties (model checking and satisfiability complexity are EXPTIME-complete and in UP co-UP respectively, same as AMC), and showing its relationship to existing logics (subsumes AMC and PµTL, incomparable with PATL/PATL*). The section 'Deciding Satisfiability' outlines a reduction to parity games, which is a theoretical contribution. While the abstract mentions an implemented tool and experimental results available in a 'full version' online, the provided text itself does not detail or present any empirical studies, data analysis, or performance metrics from actual experiments. Its core content is theoretical. |
| Researcher Affiliation | Academia | Fu Song,1 Yedi Zhang,1 Taolue Chen,2,5 Yu Tang,3 Zhiwu Xu4 1Shanghai Tech University, 2University of London 3East China Normal University, 4Shenzhen University 5Nanjing University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes logical reductions and constructions in prose and mathematical notation. |
| Open Source Code | Yes | Due to space restriction, proofs and experimental results are given in the full version, which, together with our tool PAMC and benchmarks, is available at http://faculty.sist. shanghaitech.edu.cn/faculty/songfu/Projects/PAMCSolver. |
| Open Datasets | No | The paper discusses an 'Application: Genetic Regulatory Networks' as an example to demonstrate the usage of PCGSs and PAMC, but it does not describe using a specific public dataset for any experiments, nor does it provide access information for such a dataset. It refers to 'experimental results' being available online but does not detail what dataset these results were obtained on within the provided text. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. While it mentions 'experimental results' are available online, the provided text does not contain any details on how data was partitioned for these experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions implementing a 'satisfiability solver for PAMC' and calls it 'PAMCSolver', but it does not list any specific software dependencies or their version numbers (e.g., programming languages, libraries, frameworks, or operating systems used for the implementation). |
| Experiment Setup | No | The paper does not provide specific details about the experimental setup, such as hyperparameter values, training schedules, or system-level configuration settings. The text focuses on the theoretical aspects of the logic and its decision procedures. |