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Speedup learning seeks to improve the efficiency of search-based problem solvers. In this paper, we propose a new theoretical model of speedup learning which captures systems that improve problem(More)
and time. We formalize a model for supervised learning of action strategies in dynamic stochastic domains, and show that pat-learning results on Occam algorithms hold in this model as well. We then(More)
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