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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Personalized Alert Agent for Optimal User Performance
Authors: Avraham Shvartzon, Amos Azaria, Sarit Kraus, Claudia Goldman, Joachim Meyer, Omer Tsimhoni
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an experiment using a spaceship computer game, we show that receiving alerts from the personalized alert agent significantly improves user performance. |
| Researcher Affiliation | Collaboration | Avraham Shvartzon1, Amos Azaria2, Sarit Kraus1, Claudia V. Goldman3, Joachim Meyer4 and Omer Tsimhoni3 1 Dept. of Computer Science, Bar-Ilan University, Ramat Gan 52900, Israel 2 Dept. of Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213 3 General Motors Advanced Technical Center, Herzliya 46725, Israel 4 Dept. of Industrial Engineering, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel |
| Pseudocode | No | The paper presents formal models and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code or links to a code repository. |
| Open Datasets | No | The paper uses data collected from a custom 'spaceship game' and mentions collecting 'training data from 20 subjects' but does not provide access information (link, citation, or repository) for this dataset to be publicly available. |
| Dataset Splits | No | The paper mentions 'tenfold-crossvalidation on the training data' but does not provide specific percentages, sample counts, or explicit details about the dataset splits (training, validation, test) needed for full reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., libraries, frameworks, or solvers with versions) used in the experiments. |
| Experiment Setup | Yes | Table 2: Settings used in the spaceship game: Action, Score ($), Time cost (spaceship frozen). For example: Maintenance $5 8 secs, Repair $500 3 secs, Hit by meteor $10 N/A, Hit a meteor +$30 N/A. |