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\(T_{\mathcal{P}}\)-compilation for inference in probabilistic logic programs. (English) Zbl 1386.68174
Summary: We propose \(T_{\mathcal{P}}\)-compilation, a new inference technique for probabilistic logic programs that is based on forward reasoning. \(T_{\mathcal{P}}\)-compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. The main difference with existing inference techniques for probabilistic logic programs is that these are a sequence of isolated transformations. Typically, these transformations include conversion of the ground program into an equivalent propositional formula and compilation of this formula into a more tractable target representation for weighted model counting. An empirical evaluation shows that \(T_{\mathcal{P}}\)-compilation effectively handles larger instances of complex or cyclic real-world problems than current sequential approaches, both for exact and anytime approximate inference. Furthermore, we show that \(T_{\mathcal{P}}\)-compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model.

MSC:
68T37 Reasoning under uncertainty in the context of artificial intelligence
68N17 Logic programming
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