## Learning Stochastic Logic Programs (2000)

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Citations: | 1057 - 69 self |

### BibTeX

@MISC{Muggleton00learningstochastic,

author = {Stephen Muggleton},

title = {Learning Stochastic Logic Programs},

year = {2000}

}

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### Abstract

Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic context-free grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first-order range-restricted definite clause. This paper summarises the syntax, distributional semantics and proof techniques for SLPs and then discusses how a standard Inductive Logic Programming (ILP) system, Progol, has been modied to support learning of SLPs. The resulting system 1) nds an SLP with uniform probability labels on each definition and near-maximal Bayes posterior probability and then 2) alters the probability labels to further increase the posterior probability. Stage 1) is implemented within CProgol4.5, which differs from previous versions of Progol by allowing user-defined evaluation functions written in Prolog. It is shown that maximising the Bayesian posterior function involves nding SLPs with short derivations of the examples. Search pruning with the Bayesian evaluation function is carried out in the same way as in previous versions of CProgol. The system is demonstrated with worked examples involving the learning of probability distributions over sequences as well as the learning of simple forms of uncertain knowledge.

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(Show Context)
Citation Context ...c grammars (Lari & Young 1990) and Hidden Markov Models (HMMs)) and b) declarative representations of uncertain statements (eg. probabilistic logics (Fagin & Halpern 1989) and Relational Bayes’ nets (=-=Jaeger 1997-=-)). Stochastic Logic Programs (SLPs) (Muggleton 1996) were introduced originally a way of lifting stochastic grammars (type a representations) to the level of first-order Logic Programs (LPs). Later C... |

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Citation Context ...criptions of sampling distributions (eg. stochastic grammars (Lari & Young 1990) and Hidden Markov Models (HMMs)) and b) declarative representations of uncertain statements (eg. probabilistic logics (=-=Fagin & Halpern 1989-=-) and Relational Bayes’ nets (Jaeger 1997)). Stochastic Logic Programs (SLPs) (Muggleton 1996) were introduced originally a way of lifting stochastic grammars (type a representations) to the level of ... |

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(Show Context)
Citation Context ...hastic Logic Programs (SLPs) (Muggleton 1996) were introduced originally a way of lifting stochastic grammars (type a representations) to the level of first-order Logic Programs (LPs). Later Cussens (=-=Cussens 1999-=-) showed that SLPs can be used to represent undirected Bayes’ nets (type representations). SLPs are presently used (Muggleton 2000) to define distributions for sampling within Inductive Logic Programm... |

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