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 [1].
Fast-Tracking Stationary MOMDPs for Adaptive Management Problems
Authors: Martin Pron, Kai Becker, Peter Bartlett, Iadine Chads
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the benefits of our approach by using it to initialize the solvers MO-SARSOP and Perseus on a novel computational sustainability problem and a recent adaptive management data challenge. Our approach leads to an improved initial value function and translates into significant computational gains for both solvers. [...] We compare the modified solvers (marked with a + ) with the original solvers through the quality of their initialization (Table 1) and their convergence speed (Fig. 5). |
| Researcher Affiliation | Collaboration | 1Queensland University of Technology, Brisbane QLD 4000, Australia (EMAIL) 2CSIRO, Dutton Park QLD 4102, Australia (EMAIL) 3University of Strathclyde, Glasgow G1 1XQ, United Kingdom (EMAIL) 4University of California, Berkeley, CA, United States (EMAIL) |
| Pseudocode | Yes | Algorithm 1 Calculation of the function Init |
| Open Source Code | No | The paper does not provide a specific link or statement confirming the release of their own source code for the described methodology. It only references a third-party package (MDPSolve) that they used: 'We programmed our approach with the MOMDP solver MO-SARSOP [...] with the MDPSolve package (https://sites.google.com/site/mdpsolve/)'. |
| Open Datasets | Yes | The data is freely available at goo.gl/6f4Rh0. |
| Dataset Splits | No | The paper describes the problem instances used for evaluation but does not specify explicit training, validation, or test dataset splits, percentages, or cross-validation methods. |
| Hardware Specification | Yes | Experiments conducted on a dual 3.46GHz Intel Xeon X5690 with 96GB of memory. |
| Software Dependencies | No | The paper mentions specific software like MO-SARSOP, Perseus, and the MDPSolve package but does not provide specific version numbers for any of them (e.g., 'MO-SARSOP vX.Y' or 'Perseus 1.2'). |
| Experiment Setup | Yes | We programmed our approach with the MOMDP solver MO-SARSOP (Kurniawati, Hsu, and Lee 2008; Ong et al. 2010) with the MDPSolve package (https://sites.google.com/site/mdpsolve/) and POMDP solver Perseus with 500 beliefs states (Spaan and Vlassis 2005). [...] The observable component x X specifies the season (wet/dry) and the presence or absence of the mosquitoes across the islands N (|X| = 2N+1 + 1). The component y Y is the unknown true transition function, with |Y | = 8; [...] We set γ = 0.999. |