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..
Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs
Authors: Philipp Robbel, Frans Oliehoek, Mykel Kochenderfer
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Evaluation We evaluate our methods on undirected disease propagation graphs with 30 and 50 nodes. For the first round of experiments, we contrast runtimes of the normal VE/ALP method (where possible) with those that exploit anonymous influence . We then consider a disease control problem with 25 agents in a densely connected 50-node graph that cannot be solved with the normal ALP. |
| Researcher Affiliation | Academia | Philipp Robbel MIT Media Lab Cambridge, MA, USA Frans A. Oliehoek University of Amsterdam University of Liverpool Mykel J. Kochenderfer Stanford University Stanford, CA, USA |
| Pseudocode | No | The paper describes methods like AUGMENT and REDUCE in prose and mathematical notation within Section 4 but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | No | We use graph-tool (Peixoto 2014) to generate 10 random graphs with an out-degree k sampled from P(k) 1/k, k [1, 10]. The paper describes how the graphs were generated, but does not provide concrete access information (link, DOI, specific citation) for the generated datasets themselves. |
| Dataset Splits | No | The paper evaluates mean returns from 50 randomly sampled starting states after 200 steps of policy simulation, but it does not provide specific training/validation/test dataset splits (e.g., percentages or counts) or refer to standard predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU/CPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions using 'graph-tool (Peixoto 2014)' for generating graphs, but it does not provide specific version numbers for software dependencies or libraries used in the experimental setup beyond this citation. |
| Experiment Setup | Yes | In all experiments, we use indicator functions IXi, I Xi on each state variable (covering the two valid instantiations {healthy, infected}) as the basis set H in the (RR-)ALP. We use identical transmission and node recovery rates throughout the graph, β = 0.6, δ = 0.3. Action costs are set to λ1 = 1 and infection costs to λ2 = 50. All experiments use the identical greedy elimination heuristic for both VE and RR-VE, which minimizes the scope size of intermediate terms at the next iteration. |