The algorithm plans a sequence of actions from the current state. The training instances contain examples of states and best actions of the states.
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The algorithm groups unseen data based on similarity. We have to use two inputs because the input data is two dimensional. (a) the number of examples required for learning a hypothesis in h1 is larger than the number of examples required for h (b) the number of examples required for learning a hypothesis in h1 is smaller than the number of examples required for h2. Depends on the type of problem.
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