In this paper, we present an algorithm that discovers action rules from a decision table. Action rules describe possible transitions
of objects from one state to another with respect to a distinguished attribute. The previous research on action rule discovery
required the extraction of classification rules before constructing any action rule. The new proposed algorithm does not require
pre-existing classification rules, and it uses a bottom up approach to generate action rules having minimal attribute involvement.