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..
Multi-Agent Planning with Baseline Regret Minimization
Authors: Feng Wu, Shlomo Zilberstein, Xiaoping Chen
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on common benchmark problems confirm the benefits of the algorithm compared with the state-of-the-art approaches. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, University of Science and Technology of China, CHN College of Information and Computer Sciences, University of Massachusetts Amherst, USA |
| Pseudocode | Yes | Algorithm 1: Iterative Belief Generation |
| Open Source Code | No | No statement about releasing open-source code for the described methodology is found. |
| Open Datasets | Yes | We empirically evaluated our algorithm on four common benchmark problems1 widely used in the DEC-POMDP literature: Broadcast Channel, Recycling Robots, Cooperative Box Pushing, and Meeting in a 3 3 Grid. 1http://masplan.org/problem_domains |
| Dataset Splits | No | The paper discusses running experiments on benchmark problems and problem instances, but does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | Our algorithm (i.e., IBG-DP) was implemented in Java 1.8 and ran on a machine with 3.5GHz Intel Core i7 CPU and 8GB of RAM. |
| Software Dependencies | Yes | Our algorithm (i.e., IBG-DP) was implemented in Java 1.8 and ran on a machine with 3.5GHz Intel Core i7 CPU and 8GB of RAM. The MILP and LP were solved by IBM CPLEX 12.61. |
| Experiment Setup | Yes | The baseline policies that we used here are random policy trees with max Tree = 3. |