Agents need to know the effects of their actions. Strong associations between actions and effects can be found by counting how often they co-occur We present an algorithm that learns temporal patterns expressed as fluents, propositions with temporal extent. The fluent-learning algorithm is hierarchical and unsupervised. It works by maintaining co-occurrence statistics on pairs of fluents. In experiments on a mobile robot, the fluent-learning algorithm found temporal associations that correspond to effects of the robot's actions.
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