Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract)
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This paper describes research in Probabilistic Inductive Logic Programing (PILP). The question investigated is whether PILP should always be used to learn from categorical examples. The data sets used by most PILP systems and applications have non-probabilistic class values, like those used in ILP systems. The main reason for this is the lack of an obvious source of probabilistic class values. In this context, we investigate the use of Abductive Stochastic Logic Programs (SLPs) for metabolic network learning.
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