Notice: 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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty
Authors: Daniel de Leng, Fredrik Heintz2760-2767
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach is empirically evaluated by considering the time and space requirements, as well as the impact of permitting varying degrees of uncertainty. |
| Researcher Affiliation | Academia | Daniel de Leng, Fredrik Heintz Department of Computer and Information Science Link oping University, 581 83 Link oping, Sweden EMAIL |
| Pseudocode | Yes | Algorithm 1: Classical Progression Algorithm 2: Approximate Partial-State Progression |
| Open Source Code | Yes | The jprogress implementation is available at https://github.com/dnleng/jprogress. |
| Open Datasets | No | The paper describes generating streams with specific probabilities (e.g., 'stream in which 80% of the samples are p and the remaining samples are unknown'). It does not use a named, publicly available dataset with concrete access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. It describes characteristics of the input stream and termination conditions for the progression procedure. |
| Hardware Specification | Yes | We performed our experiments using a fourth-generation Intel Xeon E5-1650 CPU (6 cores, 12 threads) with 50Gi B of RAM allocated to the JVM. |
| Software Dependencies | No | The paper states the implementation was 'in Java1' but does not provide a specific version number for Java or any other software dependencies. |
| Experiment Setup | Yes | We ο¬rst compare the runtime and space requirements given a formula and a stream for varying values for the parameters MAX TTL and MAX NODES. ... The choices for MAX NODES limit the leaked mass to at most 1% of the total probability mass. |