Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Temporal Logics Over Finite Traces with Uncertainty
Authors: Fabrizio M Maggi, Marco Montali, Rafael Peñaloza10218-10225
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,523 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,152 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. |
| Researcher Affiliation | Academia | Fabrizio M. Maggi University of Tartu EMAIL; Marco Montali Free University of Bozen-Bolzano EMAIL; Rafael Pe naloza University of Milano-Bicocca EMAIL |
| Pseudocode | Yes | Algorithm 1: Most likely scenario for t over Φ. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing its code for the described methodology. It mentions |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with specific datasets. While it discusses "event log data" in the context of potential applications, it does not provide access information for a dataset used in experiments within this paper. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware. No hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical, focusing on logical frameworks and automata. It does not describe any specific software implementations or dependencies with version numbers that would be required to reproduce experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |