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.
Read Online or Download Inductive Logic Programming: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers PDF
Similar logic books
Statistical Estimation of Epidemiological Risk provides insurance of an important epidemiological indices, and contains contemporary advancements within the field. A useful reference resource for biostatisticians and epidemiologists operating in ailment prevention, because the chapters are self-contained and have quite a few genuine examples.
This paintings introduces the topic of formal good judgment when it comes to a approach that's "like syllogistic logic". Its process, like outdated, conventional syllogistic, is a "term logic". The authors' model of good judgment ("term-function logic", TFL) stocks with Aristotle's syllogistic the perception that the logical kinds of statements which are all in favour of inferences as premises or conclusions could be construed because the results of connecting pairs of phrases via a logical copula (functor).
- Verilog HDL Synthesis A Practical Primer
- Vagueness in Context
- Perspectives in Mathematics
- Logic Design. A Review Of Theory And Practice
- A Transfinite Type Theory with Type Variables
Extra resources for Inductive Logic Programming: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers
While LPADs can be converted to ProbLog programs , 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.