Download Inductive Logic Programming: 20th International Conference, by Michael Kifer (auth.), Paolo Frasconi, Francesca A. Lisi PDF
By Michael Kifer (auth.), Paolo Frasconi, Francesca A. Lisi (eds.)
This ebook constitutes the completely refereed post-proceedings of the twentieth foreign convention on Inductive good judgment Programming, ILP 2010, held in Florence, Italy in June 2010. The eleven revised complete papers and 15 revised brief papers provided including abstracts of 3 invited talks have been conscientiously reviewed and chosen in the course of rounds of refereeing and revision. All present concerns in inductive good judgment programming, i.e. in common sense programming for laptop studying are addressed, specifically statistical studying and different probabilistic methods to computer studying are reflected.
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Extra resources for Inductive Logic Programming: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers
Sample text
While LPADs can be converted to ProbLog programs [1], the result of the conversion may contain non ground probabilistic facts so ProbLog’s Monte Carlo algorithm may not always be used. Our Monte Carlo algorithm for LPADs uses a meta-interpreter that keeps a partial explanation containing atomic choices for the disjunctive clauses sampled up to that point. The meta-interpreter is realized by Function solve in Algorithm 1 and returns 1 if the list of atoms of the goal is derivable in the sample and 0 otherwise.
Machine Learning, an Artificial Intelligence Approach, vol. 1, pp. 41–81 (1983) 6. : Selective reformulation of examples in concept learning. In: Proc. of ICML 1994, pp. 352–360 (1994) 7. : Are substitutions the better examples? Learning complete sets of clauses with Frog. In: Proc. of ILP 1995, pp. 453–474 (1995) 8. : Quantitative pharmacophore models with inductive logic programming. Machine Learning 64(1-3), 65–90 (2006) 9. : An integrated approach to feature invention and model construction for drug activity prediction.
The algorithm is guaranteed to terminate because the same head will be eventually sampled for each couple of identical groundings of a clause. Also note that the sampling distribution is not affected since inconsistency arises independently of the success or failure of a query. In Algorithm 2, consistent(Explan) returns true if Explan is consistent while sample(C) samples a head index for clause C. solve is called repeatedly to obtain the samples of truth values for the goal. The fraction of true values is an estimation of the probability of the query of interest.