An Inductive Logic Programming Approach to Statistical by K. Kersting

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 synthetic Intelligence study. This includes taking 3 disparate significant components of analysis and trying a fusion between them. the 3 parts are: common sense Programming, Uncertainty Reasoning and computer studying. each one of these is a big sub-area of study with its personal linked foreign examine meetings. Having taken on one of these Herculean activity, Kersting has produced a sequence of effects that are now on the middle of a newly rising zone: Probabilistic Inductive common sense Programming. the hot region is heavily tied to, although strictly subsumes, a brand new box often called 'Statistical Relational studying' which has within the previous few years received significant prominence within the American synthetic Intelligence learn group. inside this booklet, the writer makes numerous significant contributions, together with the advent of a chain of definitions which circumscribe the hot region shaped by way of extending Inductive common sense Programming to the case within 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

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For this case, a complete search of the space ordered by θ-subsumption is performed until all clauses cover all examples [De Raedt and Dehaspe, 1997]. While top-down approaches successively specialize a very general starting hypothesis, bottom-up approaches successively generalize a very specific hypothesis. This is basically done by deleting literals (or clauses), by turning constants into variables and/or bounded variables into new variables. Reconsider for instance the learning from proofs setting.

P (a | H, B) = s∈Dq s is a proof for a vs . 12 with uniform probability values for each predicate. The value vu of the 1 . 12 is vu = 13 · 12 · 12 = 12 s1 , s2 of atoms over the predicate sentence are those of sentence([a, turtle, sleeps], []) and sentence([the, turtle, sleeps], []) . §2 Probabilistic Inductive Logic Programming 24 Both get the value vs1 = vs2 = sentences, 1 12 . Because there is only one proof for each of the P (sentence([the, turtles, sleep], [])) = vu = 1 . 3 ◦ For stochastic logic programs, there are at least two natural learning settings.

5, f1 1, 7). 8, f1 9, 2). 8, f1 10, 2). 1, f1 11, 1). 11, f1 12, 2). 11, f1 13, 1). Consider now the positive example mutagenic(225). It is covered by H mutagenic(M) : − nitro(M, R1), logp(M, C), C > 1. together with the background knowledge B, because H ∪ B entails the example. To see this, we unify mutagenic(225) with the clause’s head. This yields mutagenic(225) : − nitro(225, R1), logp(225, C), C > 1. Now, nitro(225, R1) unifies with the fifth ground atom (left-hand side column) in B, and logp(225, C) with the fourth one.

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