Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The
  Best Input Parameters For R* Algorithm Through Series Of Experiments
  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.
