The analysis of LRTS consisted of determining if the algorithm selects the optimal first move from the initial node.
In addition we compare the performance of the LRTS algorithm and the minimin algorithm used in the model.
The experiments thus analyse the influence of the amount of granularity and type of heuristic function on the performance of the LRTS algorithm.
Pessimistic functions were found to be less prone to a lookahead pathology ; therefore, the use of this heuristic function should improve the performance of the LRTS algorithm.
The figure shows how the three heuristic functions and the granularity contribute to the LRTS algorithm's quality of search.