The adequate representation of states in the construction of intelligent agents is fundamental for allowing them to achieve a satisfactory performance, principally for those that actuate in a competitive environment that possesses a high state space. One particular type of representation that is very appropriate for these situations is the NetFeatureMap, which describes by means of features the relevant aspects that are inherent to the environment where the agent actuates. In renowned intelligent agents, such features are manually selected, which certainly leads to inadequate choices. In this way, the main contribution of this paper is to propose a new approach, based on Association Rules, that automatically selects these features. Under the intent of investigating the efficacy of such a proposal, the authors utilize the domain of Checkers player agents as their study laboratory. The best performance of the Association Rules-based agents proves the efficacy of the present proposal.