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.
Solving Partially Observable Stochastic Games with Public Observations
Authors: Karel Horák, Branislav Bošanský2029-2036
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,819 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,191 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 | Experimental results show that our algorithm can closely approximate the value of non-trivial games with hundreds of states. |
| Researcher Affiliation | Academia | Karel Hor ak, Branislav Boˇsansk y Department of Computer Science, Faculty of Electrical Engineering Czech Technical University in Prague EMAIL |
| Pseudocode | Yes | Algorithm 1: HSVI algorithm for PO-POSGs. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is open or publicly available. |
| Open Datasets | No | The paper describes generating instances for the Patrolling and Lasertag domains (e.g., 'generate random graphs from the Dorogovtsev Mendes model', 'randomly placed obstacles') rather than using pre-existing, publicly available datasets for which access information is provided. |
| Dataset Splits | No | The paper describes the construction of game instances and parameters for experiments but does not specify dataset splits (e.g., training, validation, test percentages or counts) in the typical machine learning sense. |
| Hardware Specification | Yes | All experiments use discount factor γ = 0.95 and were run on Intel i7-8700K (solving 6 instances in parallel). |
| Software Dependencies | No | The paper discusses the use of linear programming and implies the use of a solver but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | All experiments use discount factor γ = 0.95... In our experimental evaluation, we consider t = 3 and t = 4... We ran the algorithm with ϵ = 0.05 for 5 hours. |