By K. Kersting
During this e-book, the writer Kristian Kersting has made an attack on one of many toughest integration difficulties on the middle of man-made Intelligence learn. This includes taking 3 disparate significant components of study and making an attempt a fusion between them. the 3 parts are: good judgment Programming, Uncertainty Reasoning and computing device studying. most of these is an immense sub-area of study with its personal linked overseas learn meetings. Having taken on this kind of Herculean activity, Kersting has produced a chain of effects that are now on the center of a newly rising sector: Probabilistic Inductive common sense Programming. the hot quarter is heavily tied to, notwithstanding strictly subsumes, a brand new box referred to as 'Statistical Relational studying' which has within the previous few years received significant prominence within the American man made Intelligence learn neighborhood. inside of this e-book, the writer makes numerous significant contributions, together with the creation of a sequence of definitions which circumscribe the recent quarter shaped via extending Inductive good judgment Programming to the case within which clauses are annotated with likelihood values. additionally, Kersting investigates the process of studying from proofs and the problem of upgrading Fisher Kernels to Relational Fisher Kernels.
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Extra resources for An Inductive Logic Programming Approach to Statistical Relational Learning
4. Thus, ILP approaches iteratively modify the current hypothesis syntactically and test it against the examples and background theory. The syntactic modiﬁcations are done using so-called reﬁnement operators [Shapiro, 1983, Nienhuys-Cheng and de Wolf, 1997], which make small modiﬁcations to a hypothesis. 13 (Reﬁnement Operator) A reﬁnement operator ρ : H → 2H takes an ◦ hypothesis H ∈ H and gives back a syntactically modiﬁed version H ∈ H of H. For clauses, generalization and specialization operators ρg and ρs are usually employed, which just basically add a literal, unify variables, and ground variables respectively which delete a literal, anti-unify variables, and replace constants with variables.
2004, Riguzzi, 2004]. Also learning Bayesian logic programs, which we will address in Part I, falls into this setting. Here, we will illustrate the structure learning of clausal Markov logic networks. Kok and Domingos  proposed a beam-search based approach for learning clausal Markov logic networks from possible examples only. , disjunction of literals. The clauses without associated weights constitute a clausal program L, and the weights the parameters λ. Starting with some initial clausal Markov logic network H = (L, λ), the parameters maximizing score(L, λ, E) are computed.
In Part II, we introduce a novel probabilistic ILP over time approach called logical hidden Markov model. Logical hidden Markov models extend hidden Markov models to deal with sequences of structured symbols in the form of logical atoms. They employ logical atoms as structured (output and state) symbols. Variables in the atoms allow one to make abstraction of speciﬁc symbols. Uniﬁcation allows one to share information among states. The contributions are the representation language and a deﬁnition of the distribution deﬁned by a logical hidden Markov model in Chapter 5.