Learning Stochastic Logic Programs

Learning Stochastic Logic Programs (2000)

Cached

Download Links

by Stephen Muggleton

Citations:

962 - 56 self

Versions

Version History

Metadata Version 4

User correction supplied by mph

Datum	Value	Source
TITLE	Learning Stochastic Logic Programs	user correction
AUTHOR NAME	Stephen Muggleton	user correction
AUTHOR AFFIL	DEPARTMENT OF COMPUTER SCIENCE	user correction
AUTHOR ADDR	York, YO1 5DD, United Kingdom	user correction
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.	user correction
YEAR	2000	user correction
CITATIONS	9 found	ParsCit 1.0