Ensemble learning with Asa H

Various Asa H experiments have employed ensemble learning.  Perhaps the simplest averages the output from two or more individual Asa H agents.  These may have different similarity measures for instance or have been trained separately. Ensemble learning is also possible within a single Asa agent.  The N best case matches can be followed, for example, and the output can be generated by voting, averaging, interpolation, or the like.  Weighting of the individual outputs by the degree of case match and case utility can be employed. Again, as a rule groups make better decisions than individuals do.