By K. Kersting
During this ebook, the writer Kristian Kersting has made an attack on one of many toughest integration difficulties on the center of man-made Intelligence examine. This comprises taking 3 disparate significant parts of study and trying a fusion between them. the 3 components are: good judgment Programming, Uncertainty Reasoning and laptop studying. each one of these is an enormous sub-area of analysis with its personal linked overseas examine meetings. Having taken on this sort of Herculean activity, Kersting has produced a sequence of effects that are now on the middle of a newly rising sector: Probabilistic Inductive good judgment Programming. the recent zone is heavily tied to, even though strictly subsumes, a brand new box referred to as 'Statistical Relational studying' which has within the previous couple of years received significant prominence within the American man made Intelligence examine group. inside this e-book, the writer makes a number of significant contributions, together with the creation of a sequence of definitions which circumscribe the recent region shaped through extending Inductive good judgment Programming to the case during which clauses are annotated with chance values. additionally, Kersting investigates the method of studying from proofs and the problem of upgrading Fisher Kernels to Relational Fisher Kernels.
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Extra info for An Inductive Logic Programming Approach to Statistical Relational Learning
A deﬁnite clause program) that is provided to the inductive logic programming system and ﬁxed during the learning process. The hypothesis H together with the background theory B should cover all positive and none of the negative examples. Top-down approaches are for instance often employed by ILP systems that learn from entailment. More precisely, these systems often employ a separate-and-conquer rule-learning strategy [F¨ urnkranz, 1999]. In an outer loop of the algorithm, they follow a set-covering approach [Mitchell, 1997] in which they repeatedly search for a rule covering many positive examples and none of the negative examples.
Daughter(dorothy, brian). Neg daughter(rex, ann). daughter(rex, brian). Additionally, we have some general knowledge called background knowledge B, which describes the family relationships and sex of each person: mother(ann, dorothy). female(dorothy). female(ann). mother(ann, rex). father(brian, dorothy). father(brian, rex). From this information, we could induce H daughter(C, P) : − female(C), mother(P, C). daughter(C, P) : − female(C), father(P, C). , Pos are entailed by H together with B, but Neg are not entailed.
Here, we focus on Domingos and Richardson’s  Markov logic networks (MLNs) as the probabilistic ILP system. Bayesian logic programs will be discussed in detail in Part I. Markov logic networks combine Markov networks [Pearl, 1991], which represent probability distributions over propositional interpretations, with ﬁrst order logic. , interpretations: if a world violates one formula, it is less probable but not necessarily impossible as in classical logic. The fewer formulas a world violates, the more probable it is.