Numerical reasoning with an ILP system capable of lazy evaluation and customised search

• Type: Article in International Scientific Journal
• Date: 1999

Authors

Ashwin Srinivasan (Author) Other; Rui Camacho (Author) FEUP

Journal

Title: Journal of Logic Programming

Vol. 40 No. 2

Pages: 185-213

ISSN: 0743-1066

Publisher: Elsevier B.V.

Indexing

ISI Web of Knowledge - 20 Citations

ISI Web of Science

Scopus - 26 Citations

Other information

Authenticus ID: P-001-3RE

DOI: 10.1016/S0743-1066(99)00018-7

Abstract (EN): Using problem-speci®c background knowledge, computer programs developed within the framework of Inductive Logic Programming (ILP) have been used to construct restricted ®rst-order logic solutions to scienti®c problems. However, their approach to the analysis of data with substantial numerical content has been largely limited to constructing clauses that: (a) provide qualitative descriptions (``high'', ``low'' etc.) of the values of response variables; and (b) contain simple inequalities restricting the ranges of predictor variables. This has precluded the application of such techniques to scienti®c and engineering problems requiring a more sophisticated approach. A number of specialised methods have been suggested to remedy this. In contrast, we have chosen to take advantage of the fact that the existing theoretical framework for ILP places very few restrictions of the nature of the background knowledge. We describe two issues of implementation that make it possible to use background predicates that implement well-established statistical and numerical analysis procedures. Any improvements in analytical sophistication that result are evaluated empirically using arti®cial and real-life data. Experiments utilising arti®cial data are concerned with extracting constraints for response variables in the text-book problem of balancing a pole on a cart. They illustrate the use of clausal de®nitions of arithmetic and trigonometric functions, inequalities, multiple linear regression, and numerical derivatives. A non-trivial problem concerning the prediction of mutagenic activity of nitroaromatic molecules is also examined. In this case, expert chemists have been unable to devise a model for explaining the data. The result demonstrates the combined use by an ILP program of logical and numerical capabilities to achieve an analysis that includes linear modelling, clustering and classi®cation. In all experiments, the predictions obtained compare favourably against benchmarks set by more traditional methods of quantitative methods, namely, regression and neural-network.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 28