Extended Increasing Cost Tree Search for Non-Unit Cost Domains

Authors: Thayne T. Walker, Nathan R. Sturtevant, Ariel Felner

IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our experiments show that higher quality sub-optimal solutions are achievable in domains with finely discretized movement models in no more time than lower-quality, optimal solutions in domains with coarsely discretized movement models.
Researcher Affiliation Academia 1 University of Denver, Denver, CO, USA 2 Ben-Gurion University, Be er-Sheva, Israel
Pseudocode Yes Algorithm 1 shows pseudo code for the reformulated high level search. [...] Algorithm 2 reformulated ICTS Low Level Search JOINTDFS
Open Source Code No The paper does not provide concrete access to source code through a specific repository link, explicit code release statement, or mention of code in supplementary materials.
Open Datasets No The paper mentions "Test sets consist of 100 random instances of the MAPF problem with varying numbers of agents on 4, 8, 16, and 32-connected grid domains also known as 2k neighborhoods [Rivera, Hern andez, and Baier, 2017]". While it identifies the type of domain, it does not provide specific access information (link, repository, or detailed citation) for the exact dataset instances or generation process to reproduce the data.
Dataset Splits No The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It only refers to "Test sets."
Hardware Specification Yes All empirical tests were conducted on a machine with 64 Intel Xeon (r) cores at 2.2GHz with 128 GB of RAM.
Software Dependencies No The paper mentions algorithms like A*, CBS, and ICTS, but does not provide specific version numbers for any software, libraries, or programming languages used in the experiments.
Experiment Setup Yes Both A* and ICTS use OD-style branching and the ID framework, solving only conflicting agents jointly. The CBS algorithm is using continuous-time collision detection and the conflict prioritization (PC) enhancement from ICBS [Boyarski et al., 2015]. [...] All algorithms use a time resolution of r=10 3. The ICTS algorithm is using an increment of δ = 1 and the simple pairwise pruning enhancement.