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..
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. |