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..
Dec-SGTS: Decentralized Sub-Goal Tree Search for Multi-Agent Coordination
Authors: Minglong Li, Zhongxuan Cai, Wenjing Yang, Lixia Wu, Yinghui Xu, Ji Wang11282-11289
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct the experiments on courier dispatching problem, and the results show that Dec-SGTS achieves much better reward while enjoying a significant reduction of planning time and communication cost compared with Dec-MCTS (Decentralized Monte Carlo Tree Search). |
| Researcher Affiliation | Collaboration | Minglong Li, 1 Zhongxuan Cai, 1 Wenjing Yang, 1 Lixia Wu,2 Yinghui Xu,2 Ji Wang1 1 Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, China 2 Artificial Intelligence Department, Zhejiang Cainiao Supply Chain Management Co., Ltd., China |
| Pseudocode | Yes | Algorithm 1 Dec-SGTS for agent i" and "Algorithm 2 Expansion with Subgoal States |
| Open Source Code | Yes | For formalization and proof, see supplementary material https://github.com/HPCL-micros/dec-sgts. |
| Open Datasets | No | The paper mentions using "CDP benchmark" and shows examples from "Amap" and "CDP grid world" but does not provide a direct link, DOI, specific repository name, or formal citation for accessing the dataset used in experiments. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) is provided. |
| Hardware Specification | Yes | Dec-SGTS is implemented with a platform of 12 cores, 3.7 GHz and 16 GB Memory. |
| Software Dependencies | No | No specific ancillary software details, such as library or solver names with version numbers, are provided. |
| Experiment Setup | Yes | Agent moves for one grid with reward -0.01 and picks up a package with reward 1.0. For each experiment, we use different settings and parameters. Given planning time of 15 seconds. We use the action coverage threshold σ to expand tree nodes with subgoal states. |