
Probabilistic Logic Programming under Inheritance with Overriding
We present probabilistic logic programming under inheritance with overri...
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A note on the Declarative reading(s) of Logic Programming
This paper analyses the declarative readings of logic programming. Logic...
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Implementing Default and Autoepistemic Logics via the Logic of GK
The logic of knowledge and justified assumptions, also known as logic of...
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Loglinear models for firstorder probabilistic reasoning
Recent work on loglinear models in probabilistic constraint logic progra...
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MAP Inference for Probabilistic Logic Programming
In Probabilistic Logic Programming (PLP) the most commonly studied infer...
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Viterbi training in PRISM
VT (Viterbi training), or hard EM, is an efficient way of parameter lear...
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Nonmonotonic Negation in Probabilistic Deductive Databases
In this paper we study the uses and the semantics of nonmonotonic negat...
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Probabilistic Disjunctive Logic Programming
In this paper we propose a framework for combining Disjunctive Logic Programming and Poole's Probabilistic Horn Abduction. We use the concept of hypothesis to specify the probability structure. We consider the case in which probabilistic information is not available. Instead of using probability intervals, we allow for the specification of the probabilities of disjunctions. Because minimal models are used as characteristic models in disjunctive logic programming, we apply the principle of indifference on the set of minimal models to derive default probability values. We define the concepts of explanation and partial explanation of a formula, and use them to determine the default probability distribution(s) induced by a program. An algorithm for calculating the default probability of a goal is presented.
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