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Learning Classifier Systems use reinforcement learning, evolutionary computing and/or heuristics to develop adaptive systems. This paper extends the ZCS Learning Classifier System to improve its internal modelling capabilities. Initially, results are presented which show performance in a traditional reinforcement learning task incorporating lookahead within the rule structure. Then a mechanism for effective learning without external reward is examined which enables the simple learning system todoi:10.1145/2464576.2483909 dblp:conf/gecco/BrowneU13 fatcat:imns7hwz7vbm5lzny6je26t3n4