Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments
Abstract
Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it. The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*). Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters. In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance. Then we evaluate these assumptions by running a large number of experiments. As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm's best performance.
Cite
@article{arxiv.1511.00840,
title = {Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments},
author = {Konstantin Yakovlev and Egor Baskin and Ivan Hramoin},
journal= {arXiv preprint arXiv:1511.00840},
year = {2015}
}
Comments
8 pages, 2 figures, 18 references. As accepted to the 16th International Conference on Artificial Intelligence:Methodology, Systems, Applications (AIMSA 2014), Varna, Bulgaria, September 11-13, 2014