The agent computing paradigm is rapidly emerging as one of the powerful technology to deal with the uncertainty in dynamic environment. Recently, traditional learning classifier system are challenged by changes in the context. In this paper, an anticipatory agent based on the Anticipatory Learning Classifier System (ACS) for learning in changing environments is presented. This research aims to develop an agent learning architecture using anticipatory system that will enable intelligent agent to be able to detect environmental changes, adapt functionality at run-time to achieve goal. For achieving the intended target, an extension to the ACS framework called “Greedy Covering” to the ACS framework have been proposed. The novelty of the approach is in determining the changes in the environment and to generate optimal rules to adapt and reestablish the optimal policy to reach the goal state. The proposed algorithm is evaluated on several synthetic maze design and simulate a variety of changing environments. Experiment results indicate that up to 65% changes in an environment the ACS with the greedy covering can reestablish the optimal performance without increasing the number of classifiers.