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..
Effective Integration of Weighted Cost-to-Go and Conflict Heuristic within Suboptimal CBS
Authors: Rishi Veerapaneni, Tushar Kusnur, Maxim Likhachev
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Results We test our methods with different numbers of agents, in increments of 50, on 8 diverse maps from Stern et al. (2019) and report the mean values across 5 seeds. |
| Researcher Affiliation | Academia | Rishi Veerapaneni, Tushar Kusnur, Maxim Likhachev Robotics Institute, Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1: Suboptimal CBS low level focal search planner Input: nstart, at Goal(), Paths PI of other agents Output: Lower bound LB on optimal path cost, Path from nstart with sub-optimality wso (i.e. cost wso LB) |
| Open Source Code | No | The paper mentions a detailed version with supplementary material at an arXiv link (https://arxiv.org/abs/2205.11624), but does not explicitly state that its own source code is provided there or elsewhere. |
| Open Datasets | Yes | We test our methods with different numbers of agents, in increments of 50, on 8 diverse maps from Stern et al. (2019) |
| Dataset Splits | No | The paper tests on '8 diverse maps' and reports 'mean values across 5 seeds' but does not specify explicit training/validation/test dataset splits or cross-validation methodology. |
| Hardware Specification | No | The paper states 'The speed up Smethod = Tbaseline/Tmethod is reported to normalize differences in hardware' but does not provide any specific hardware details such as CPU, GPU models, or memory. |
| Software Dependencies | No | The paper refers to using EECBS (Li, Ruml, and Koenig 2021) and its open-source codebase, but it does not specify any particular software dependencies with version numbers (e.g., Python version, library versions) used for its own implementation or experiments. |
| Experiment Setup | Yes | We use wso = 2 and a timeout of 300 seconds in all our experiments unless otherwise specified. In all figures, if a method failed (timed out on all 5 seeds) on a particle number of agents on a map, we do not report larger number of agents. |