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].
Preallocation and Planning Under Stochastic Resource Constraints
Authors: Frits de Nijs, Matthijs Spaan, Mathijs de Weerdt
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we evaluate the effect of planning for stochastic resource constraints on a single time-step search and rescue domain and on a longer horizon energy demand planning problem. We compare the modified algorithms Preallocation MILP and CMDP, designated P(X = x), with their original versions planning for the expected resource limit, E(X). |
| Researcher Affiliation | Academia | Frits de Nijs, Matthijs T. J. Spaan, Mathijs M. de Weerdt EMAIL Delft University of Technology, The Netherlands |
| Pseudocode | Yes | Algorithm 1 Resource allocation MILP for SRC-MMDP. and Algorithm 2 Constrained MMDP LP for SRC-MMDP. |
| Open Source Code | No | The paper does not provide a specific repository link, explicit code release statement, or mention of code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data for the TCL problem and a problem setup for the SAR domain, but does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using MILP solvers and Value Iteration but does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments we consider TCL problems with temperature ranges discretized into 25 states, and with agents having 4 actions, corresponding to switching a heater on for {0, 5, 10, 15} out of 15 minutes per time step. The thermal parameters are based on reference insulation levels of houses equipped with heat-pumps. To model consumer behavior and build quality variation, we add small Gaussian noise to the parameters, resulting in a heterogeneous population of TCLs. |