Action rules can be seen as logical terms describing knowledge about possible actions associated with objects that are hidden in a decision system. Classical strategy for discovering them from a database requires prior extraction of classification rules that next are evaluated, pair by pair, with a goal to build a strategy of action based on condition features, in order to get a desired effect on a decision feature. An actionable strategy is represented as a term r = [(w) ? (a?b)]?[f?y], where ?, a, ß, f, and ? are descriptions of events. The term r states that when the fixed condition ? is satisfied and the changeable behavior (a?ß) occurs in objects represented as tuples from a database, so does the expectation (f??). With each object, a number of actionable strategies can be associated, and each one of them may lead to different expectations and the same to different reclassifications of objects. This chapter will focus on a new strategy of construction of action rules directly from single classification rules instead of pairs of classification rules. This way we do not only gain on the simplicity of the method of action rules construction, but also on its time complexity. The chapter will present a modified tree-based strategy for constructing action rules, followed by a new simplified strategy of constructing them. Finally, these two strategies will be compared.