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In a deterministic world, a planning agent can be certain of the consequences of its planned sequence of actions. Not so, however, in dynamic, stochastic domains where Markov decision processes are commonly used. Unfortunately these suffer from the 'curse of dimensionality': if the state space is a Cartesian product of many small sets ('dimensions'), planning is exponential in the number of those dimensions. Our new technique exploits the intuitive strategy of selectively ignoring variousdoi:10.1613/jair.3414 fatcat:3t3ixzmcdffcjg6ibt757bqa7y