Exploratory Digraph Navigation Using A*
Authors: Fabrice Mayran de Chamisso, Laurent Soulier, Michaël Aupetit
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Experiments... We compare the performance of this algorithm to a non-exploratory strategy using A* and discuss its relation to existing algorithms such as D* Lite, PHA* with early stopping, EWP or exploration algorithms... Figure 3: Average navigational cost ( N) changes for successful experiments, random (left) and grid-like (right) graphs. |
| Researcher Affiliation | Academia | Fabrice Mayran de Chamisso CEA, LIST F-91191 Gif-sur-Yvette, France Laurent Soulier CEA, LIST F-91191 Gif-sur-Yvette, France Micha el Aupetit Qatar Computing Research Institute |
| Pseudocode | Yes | Algorithm 1 Planning with EDNA*... Algorithm 2 Navigation with EDNA* |
| Open Source Code | No | The paper describes the algorithm and experiments but does not provide any statement or link indicating the availability of open-source code for their methodology. |
| Open Datasets | No | n GR = 200 random planar digraphs GR with about 9 000 vertices and 12 500 dual-way edges each are generated, with square-shaped possibly-overlapping (SSPO) obstacles of varying size... The paper states the graphs were 'generated' but does not provide access information (link, citation, etc.) for this generated dataset to be publicly available. |
| Dataset Splits | No | The paper describes generating random planar digraphs and conducting 'informative experiments' by randomly choosing starting and goal points. It does not specify a traditional train/validation/test dataset split with percentages or sample counts for reproducing data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper describes the algorithms and their comparison but does not provide specific software dependencies (e.g., programming languages, libraries, or solvers with version numbers) used for implementation or experimentation. |
| Experiment Setup | Yes | We chose to use a risk heuristic R(O, F, Z, G) = D(O, Z) + α (1 β.cos(θ)) H (Z, F, G) in an Euclidean space with the L2 norm as H. ...uniformly sampling 100 values in [1; 9] for α and 100 values in [10%; 50%] for φ... β 0.25 was experimentally observed to give EDNA* the best navigational improvements over A*. |