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One important factor determining the computational complexity of evaluating aprobabilistic network is the cardinality of the state spaces of the nodes. Byvarying the granularity of the state spaces, one can trade off accuracy in theresult for computational efficiency. We present an anytime procedure forapproximate evaluation of probabilistic networks based on this idea. Onapplication to some simple networks, the procedure exhibits a smoothimprovement in approximation quality as computation time increases. Thissuggests that state-space abstraction is one more useful control parameter fordesigning real-time probabilistic reasoners.

State-space Abstraction for Anytime Evaluation of Probabilistic Networks