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The classical approach to the acquisition of knowledge and reason in artificial intelligence is to program the facts and rules into the machine. Unfortunately, the amount of time required to program the equivalent of human intelligence is prohibitively large. An alternative approach allows an automaton to learn to solve problems through iterative trial-and-error interaction with its environment, much as humans do. To solve a problem posed by the environment, the automaton generates a sequence or collection of responses based on its experience. The environment evaluates the effectiveness of this collection, and reports its evaluation to the automaton.