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.
Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior
Authors: Vaibhav V. Unhelkar, Julie A. Shah2522-2530
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,573 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,625 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach in experiments and demonstrate improvements in metrics of model alignment. |
| Researcher Affiliation | Academia | Vaibhav V. Unhelkar, Julie A. Shah Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA 02139 EMAIL |
| Pseudocode | No | The paper describes algorithms and equations but does not present any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for the datasets used. It describes simulated scenarios, but not how to access the data. |
| Dataset Splits | No | The paper mentions 'training and testing datasets' and 'test set' but does not provide specific percentages, sample counts, or a detailed methodology for splitting the data to reproduce the partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'pyhsmm, a Python library for approximate unsupervised inference' and 'SLSQP, a sequential least squares programming optimization algorithm as implemented in scipy', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Identical priors, hyper-parameters, initialization and termination conditions were used for all the algorithms. |