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..
Solving Partially Observable Stochastic Shortest-Path Games
Authors: Petr Tomášek, Karel Horák, Aditya Aradhye, Branislav Bošanský, Krishnendu Chatterjee
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate the algorithm on a pursuit-evasion game. |
| Researcher Affiliation | Academia | 1Artificial Intelligence Center, Dept. of Computer Science Faculty of Electrical Engineering, Czech Technical University in Prague 2Institute of Science and Technology Austria |
| Pseudocode | Yes | Algorithm 1: HSVI for discounted OS-POSGs; Algorithm 2: HSVI algorithm for POSSPGs |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper describes using a 'pursuit-evasion game' environment for evaluation, which is a simulated setting rather than a named, publicly available dataset with concrete access information (link, DOI, etc.). |
| Dataset Splits | No | The paper describes the setup of a pursuit-evasion game but does not specify any training/validation/test dataset splits for reproducibility. |
| Hardware Specification | Yes | All computational results have been obtained on computers equipped with Intel Xeon Scalable Gold 6146 processors while limiting the runtime to 10 hours and RAM to 128 GB. |
| Software Dependencies | Yes | We used CPLEX 12.9 to solve linear programs. |
| Experiment Setup | Yes | All solution methods were required to find an ϵ-optimal solution where ϵ was set to 1. Since the reward for all transitions in the game is 1, such setting allows us to find an optimal solution 1 move. |