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2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS) (2015)
Kyoto, Japan
July 6, 2015 to July 10, 2015
ISSN: 1043-6871
ISBN: 978-1-4799-8875-4
pp: 621-632
We study a natural variant of the implicational fragment of propositional logic. Its formulas are pairs of conjunctions of positive literals, related together by an implicational-like connective, the semantics of this sort of implication is defined in terms of a threshold on a conditional probability of the consequent, given the antecedent: we are dealing with what the data analysis community calls confidence of partial implications or association rules. Existing studies of redundancy among these partial implications have characterized so far only entailment from one premise and entailment from two premises. By exploiting a previously noted alternative view of this entailment in terms of linear programming duality, we characterize exactly the cases of entailment from arbitrary numbers of premises. As a result, we obtain decision algorithms of better complexity, additionally, for each potential case of entailment, we identify a critical confidence threshold and show that it is, actually, intrinsic to each set of premises and antecedent of the conclusion.
Linear programming, Semantics, Probabilistic logic, Context, Data analysis, Communities, Redundancy

A. Atserias and J. L. Balcazar, "Entailment among Probabilistic Implications," 2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), Kyoto, Japan, 2015, pp. 621-632.
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