Distributed Fleet Management in Noisy Environments via Model-Predictive Control

This object is the reproducibility package for the paper Distributed Fleet Management in Noisy Environments via Model-Predictive Control accepted for publication at ICAPS '22. The package contains the software for executing the experiments, the data presented in the paper, examples of Uppaal models, and scripts for redoing the experiments presented in the paper

Use of Artificial Intelligence for Data Extraction in Systematic Reviews

This brief study aims to validate the use of AI for data extraction in systematic reviews for RCT's in comparison with human reviewers as the reference standard. Additionally, we aim to test the reproducibility of the AI output. The validation of chatGPT will be categorized as whether we can recommend the use of ChatGPT 4o as a tool for extracting data with ChatGPT 4o being valid to use as a single reviewer, second reviewer, or third reviewer, or not valid to use as a tool for data extraction. Assessment and categorization of the responses from ChatGPT: 1. Completely correct - Correctly answers the question. - No false data. - No missing data. - No unnecessary information. 2. Satisfactory: - Correct on what we ask. - No missing data. - Additional information is acceptable, even if false, if it correctly answers our questions. 3. Lacking Information: - Partly answers our question correct. - Missing some information. - No false data. - Extra info is acceptable. 4. Missing data - Do not answer the question. - No false data. 5. False data - Directly incorrect answers, invents lies. Assessment of the validity. Single Reviewer: - 100% correct - Answers category 1 on all questions - (i.e., agreement with 2 human authors) Second Reviewer: - 80-99% correct - Answers correctly in <80% in category 2 - under 10% lies Third Reviewer: 50-79% correct - max 20% lies. Useless: - Less than 50% correct - and/or more than 20% lies

Distributed Fleet Management in Noisy Environments via Model-Predictive Control: Reproducibility package for the accepted ICAPS '22 submission

This object is the reproducibility package for the paper Distributed Fleet Management in Noisy Environments via Model-Predictive Control accepted for publication at ICAPS '22. The package contains the software for executing the experiments, the data presented in the paper, examples of Uppaal models, and scripts for redoing the experiments presented in the paper

Learning Symbolic Timed Models from Concrete Timed Data – Data and Replication Package

Artifacts for the publication “Learning Symbolic Timed Models from Concrete Timed Data”. This includes data, code, and a reproduction package