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..
Collaborative Decision Making Using Action Suggestions
Authors: Dylan Asmar, Mykel J Kochenderfer
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose two methods that use suggested actions and demonstrate the approach through simulated experiments. The proposed methodology results in increased performance while also being robust to suboptimal suggestions. |
| Researcher Affiliation | Academia | Dylan M. Asmar Stanford Intelligent Systems Laboratory Stanford University Stanford, CA 94305 EMAIL Mykel J. Kochenderfer Stanford Intelligent Systems Laboratory Stanford University Stanford, CA 94305 EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Code is available at https://github.com/sisl/action_suggestions. |
| Open Datasets | Yes | The proposed methodology to incorporate action suggestions as observations was evaluated on two classic POMDP problems, Tag [30] and Rock Sample [38]. |
| Dataset Splits | No | No explicit train/validation/test dataset splits are mentioned, as the experiments are conducted in simulated POMDP environments rather than on fixed datasets that are typically split for supervised learning. |
| Hardware Specification | No | Our approach does not require a lot of computation. Computation time and amount was not a concern and not provided. |
| Software Dependencies | No | The simulation environment was built using the POMDPs.jl framework [39]. |
| Experiment Setup | Yes | Experiment details are documented in section 4. The hyperparameter is kept constant for each simulation and the value used is shown with the presented results. |