Path Planning with CPD Heuristics
Authors: Massimo Bono, Alfonso E. Gerevini, Daniel D. Harabor, Peter J. Stuckey
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate CPD-Search in two online settings: optimal search and anytime search. In both experiments we use grid benchmarks drawn from Sturtevant s well known repository at http://movingai.com. |
| Researcher Affiliation | Academia | 1Dipartimento di Ingegneria dell Informazione, Universit a degli Studi di Brescia, Brescia, Italy 2Faculty of Information Technology, Monash University, Melbourne, Australia |
| Pseudocode | Yes | Algorithm 1: CPD-Search(w, w , s, t, ϵ): Variant of Weighted A* with a CPD heuristic. Algorithm 2: w CPD(s,t): Retrieving the original (using w) and new (using w ) cost of the shortest path (according to w) for an (s, t) pair. |
| Open Source Code | Yes | All codes are in C++ and available from https://bitbucket.org/koldar/astar-early-stop/. |
| Open Datasets | Yes | In both experiments we use grid benchmarks drawn from Sturtevant s well known repository at http://movingai.com. |
| Dataset Splits | No | The paper uses grid benchmarks with various instances (start-target pairs) for evaluation but does not specify a training, validation, or test dataset split in the typical machine learning sense. |
| Hardware Specification | Yes | Our test machine is a i78700 machine with 16GB memory. |
| Software Dependencies | No | The paper states 'All codes are in C++', but does not provide specific version numbers for any libraries, frameworks, or other software dependencies. |
| Experiment Setup | Yes | We use a radius r = 15 and a weight multiplication of w'(e) = w(e) (3e^(-x^2/45) + 1) where x is the number of hops from node n to edge e. We implement ALT with different numbers of landmark nodes, up to diminishing returns: 6, 12 and 18. |