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Absolute Versus Probabilistic Classification in a Logical Setting

机译:逻辑环境中的绝对与概率分类

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摘要

Given a set W of logical structures, or possible worlds, a set D of logical formulas, or possible data, and a logical formula φ, we consider the classification problem of determining in the limit and almost always correctly whether a possible world M satisfies φ, from a complete enumeration of the possible data that are true in M. One interpretation of almost always correctly is that the classification might be wrong on a set of possible worlds of measure 0, w.r.t. some natural probability distribution over the set of possible worlds. Another interpretation is that the classifier is only required to classify a set W′ of possible worlds of measure 1, without having to produce any claim in the limit on the truth of φ in the members of the complement of W′ in W. We compare these notions with absolute classification of W w.r.t. a formula that is almost always equivalent to φ in W, hence investigate whether the set of possible worlds on which the classification is correct is definable. Finally, in the spirit of the kind of computations considered in Logic programming, we address the issue of computing almost correctly in the limit witnesses to leading existentially quantified variables in existential formulas.
机译:给定逻辑结构W或可能世界的集合W,逻辑公式或可能数据的集合D以及逻辑公式φ,我们考虑在极限范围内几乎总是正确地确定可能世界M是否满足φ的分类问题,从对M中正确的可能数据的完整枚举中得出。几乎总是正确的一种解释是,在一组可能的度量世界0,wrt上分类可能是错误的在可能世界范围内的一些自然概率分布。另一种解释是,仅需要分类器对一组可能的度量世界W'进行分类,而不必在W的W'的补数的成员中限制φ真值时提出任何要求。这些概念与W wrt的绝对分类一个几乎总是等于W中的φ的公式,因此研究分类正确的可能世界集是否可定义。最后,本着逻辑编程中考虑的那种计算的精神,我们几乎在极限见证人中正确地解决了计算问题,以证明存在公式中存在存在量化变量。

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