Alpha-Beta pruning is, to my knowledge, only really defined in a mini-max setup. By definition, it leverages the mini-max framework to prune areas of the search space.

You can apply the core concept -- refusing to expend computation effort when the result of that effort is guaranteed to not be used -- to any number of other methods and approaches in Machine Learning and AI.