Abstract - In many complex problems a particular decision making 
procedure is often required in order for a final solution to be found. 
Such a procedure may consist of a large number of intermediate steps 
where "local" decisions must be taken and can be sometimes represented 
as a decision tree. When that structure is used the final solutions 
obtaned vary depending on the available information. However, If the 
same model is applied many times, experimental data can be collected 
and observations on the acquired knowledge can be made. In this work, 
we present a probabilistic approach for reducing the number of decisions 
(tests) that are required in a particular decision making situation. 
Specifically, we consider that a problem is structured as a complete 
binary balanced decision tree the interior nodes of which correspond 
to decision points; the paths of the tree represent different decision 
making processes. By assuming that there exists sufficient probabilistic 
information concerning the decisions at the interior nodes, we propose 
techniques in order to minimize the average number of these decisions 
when we search for a final solution.