For example, let us consider a binary search tree, and envisage a shallow node A which is a direct child of the root-node. We will assume every node is unvisited and has a predicted score of 0.5 supplied from a value network. This score indicates that half of the subsequent nodes will be successful. For a binary tree, there will be one successful, and one failed node at each decision point. Therefore, if the tree has a terminal depth, d, and our current observed node is at depth, n, then the probability of attaining a successful route through an arbitrary simulation is 0.5 . From this, we can deduce that the probability of reaching successful routes through random simulation will increase as n approaches d. In the unmodified UCT equation, there is no consideration of how close the search is to terminating. The final iteration performs the same UCT calculation as the first iteration. This often causes the later iterations to be wasted, while performing exploration of unvisited nodes, which have a low probability of yielding a successful route. eUCT takes this into consideration and weights the search towards exploitative nodes incrementally, as the search gets closer towards termination. The adaptations to the parameter C in the unmodified UCT equation cause the value of C to vary continuously from C to C/1.5, as the current iteration progresses from 0% to 100% of the total iteration count. This alteration reduces the exploration weighting (the second term in the right-hand side of Eqn. 1) as the search progresses. The changes required for eUCT are shown in Eqn 2.