Abstract
Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Early research on action rule discovery usually required the extraction of classification rules before constructing any action rule. Newest algorithms discover action rules directly from a decision system. This paper gives a new approach for generating action rules by incorporating a pruning step through micro-actions. The notion of Micro-actions is introduced. They are nodes in a higher-level knowledge, which are linked with atomic terms showing changes within classification attributes. New influence matrix is presented and used to show the cascading effect of actions modeled as action rules. Moreover, an application of the proposed approach in education is demonstrated.
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