Finetuning Randomized Heuristic Search for 2D Path Planning: Finding the Best Input Parameters for R~* Algorithm through Series of Experiments

摘要

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.