Implicit Coordination Using FOND Planning
Authors: Thorsten Engesser, Tim Miller7151-7159
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a small case study, modeling the problem of multi-agent path finding with destination uncertainty in FOND, to show that our approach can be successfully applied in practice. [...] Figure 4 shows the running time of the planner on the example instance from Figure 3 as well as on versions with additional agents and vertices in the graph, comparing it to the DEL-based planner of Engesser et al. (2017)1. |
| Researcher Affiliation | Academia | Thorsten Engesser Department of Computer Science University of Freiburg, Germany engesser@cs.uni-freiburg.de Tim Miller School of Computing and Information Systems University of Melbourne, Australia tmiller@unimelb.edu.au |
| Pseudocode | No | The paper includes PDDL code snippets as part of the case study modeling, but it does not present structured pseudocode or algorithm blocks that describe the underlying method or algorithm. |
| Open Source Code | Yes | The PDDL files and experiment logs can be downloaded at https://gkigit.informatik.uni-freiburg.de/tengesser/mapfdu-fond. |
| Open Datasets | No | The paper describes modeling instances of the multi-agent path finding with destination uncertainty (MAPF/DU) problem. It uses a case study with custom-generated instances rather than a named, publicly available dataset with concrete access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., percentages or sample counts for training, validation, or testing) as it focuses on problem instances for planning rather than traditional dataset-based machine learning experiments. |
| Hardware Specification | No | The paper mentions that the DEL-based planner 'runs out of memory (32GB)' but does not provide specific details about the CPU, GPU, or other hardware used for the experiments. |
| Software Dependencies | No | The paper mentions using 'PDDL (Mc Dermott 1998)' and 'the my ND planner of Mattm uller et al. (2010)' but does not provide specific version numbers for these software dependencies or any other libraries. |
| Experiment Setup | No | The paper describes the modeling of the MAPF/DU problem in PDDL, including action definitions and fluent usage. However, it does not provide specific hyperparameters (like learning rates or batch sizes) or system-level training settings commonly found in machine learning experiments. |