Iterative-Deepening Conflict-Based Search
Authors: Eli Boyarski, Ariel Felner, Daniel Harabor, Peter J. Stuckey, Liron Cohen, Jiaoyang Li, Sven Koenig
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Third, we undertake an extensive empirical comparison which demonstrates practical benefits. IDCBS is able to optimally solve many more problem instances than CBS and we report substantial improvements in search times even for problem instances which can be solved by a currently leading CBS variant. |
| Researcher Affiliation | Academia | 1Ben-Gurion University of the Negev 2Monash University 3University of Southern California |
| Pseudocode | Yes | Algorithm 1: High-level of CBS |
| Open Source Code | Yes | The code is available on the mapf.info website and at https://github.com/eli-b/idcbs. |
| Open Datasets | Yes | To compare CBS and IDCBS, we experimented on the MAPF benchmark [Stern et al., 2019], which contains 32 grids with different attributes (city maps, grids with random obstacles, mazes, warehouse maps, etc.), each with a number of scenarios (i.e., start and goal locations for up to 7,000 agents). |
| Dataset Splits | No | The paper refers to increasing the number of agents and using a time limit but does not specify explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | Yes | All experiments were run on a Linux laptop with an Intel Core i7-8650U CPU running at 1.9GHz. |
| Software Dependencies | No | The paper mentions 'Gurobi Optimizer' and 'C++ code base' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | To demonstrate the advantages of IDCBS, we set a time limit of 1 hour for both algorithms and iteratively solved problem instances from the first scenario of each map in the standard MAPF benchmark [Stern et al., 2019], adding agents until we failed. We set the memory limit to 8GB to emulate the amount of memory allocated per v CPU in Amazon Elastic Compute Cloud s memory-optimized R5 instances. |