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On the hardness of approximate reasoning
1996
Artificial Intelligence
Many AI problems, when formalized, reduce to evaluating the probability that a propositional expression is true. In this paper we show that this problem is computationally intractable even in surprisingly restricted cases and even if we settle for an approximation to this probability. We consider various methods used in approximate reasoning such as computing degree of belief and Bayesian belief networks, as well as reasoning techniques such as constraint satisfaction and knowledge compilation,
doi:10.1016/0004-3702(94)00092-1
fatcat:yt4pdkftfvblbnjyenyikcks5e