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].
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability
Authors: Xia Qu, Prashant Doshi
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Five variants of EM are evaluated as appropriate: the exact EM inference-based planning (labeled as I-EM); replacing the exact M-step with its greedy variant analogously to the greedy maximization in EM for POMDPs [12] (I-EM-Greedy); iterating EM based on coordinate blocks (I-EM-BCD) and coupled with a greedy M-step (I-EM-BCD-Greedy); and lastly, using forward filtering-backward sampling (I-EM-FFBS). In Fig. 2-I(a-d), we compare the variants on all problems. |
| Researcher Affiliation | Collaboration | Xia Qu Epic Systems Verona, WI 53593 EMAIL Prashant Doshi THINC Lab, Dept. of Computer Science University of Georgia, Athens, GA 30622 EMAIL |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present any structured code-like blocks. |
| Open Source Code | No | The paper does not provide any specific links to open-source code repositories, nor does it state that the code for the described methodology is publicly available or included in supplementary materials. |
| Open Datasets | Yes | We use 4 problem domains: the noncooperative multiagent tiger problem [13]... A larger noncooperative 2-agent money laundering (ML) problem [14] forms the second domain... We also evaluate a 3-agent UAV reconnaissance problem... [8]. Finally, the recent policing protest problem is used... [15]. |
| Dataset Splits | No | The paper describes the problem domains and model configurations but does not specify dataset splits (e.g., percentages or sample counts) for training, validation, or testing, nor does it refer to predefined splits from cited sources. |
| Hardware Specification | Yes | All experiments were run on Linux with Intel Xeon 2.6GHz CPUs and 32GB RAM. |
| Software Dependencies | No | The paper mentions the operating system ('Linux') but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | We use 4 problem domains: the noncooperative multiagent tiger problem [13] (|S|= 2, |Ai|= |Aj|= 3, |Oi|= |Oj|= 6 for level l 1, |Oj|= 3 at level 0, and γ = 0.9)... using FSCs of sizes 5, 3, 9 and 5, for the 4 domains respectively demonstrated a good balance. We explored various coordinate block configurations eventually settling on 3 equal-sized blocks for both the tiger and ML, 5 blocks for UAV and 2 for policing protest. |