Improving numerical reasoning capabilities of inductive logic programming systems

Inductive Logic Programming (ILP) systems have been largely applied to classification problems with a considerable success. The use of ILP systems in problems requiring numerical reasoning capabilities has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the range of domains where the ILP paradigm may be applied. This paper proposes improvements in numerical reasoning capabilities of ILP systems. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAG method to evaluate learning performance. We have found these extensions essential to improve on results mer statistical-based algorithms for time series forecasting used in the empirical evaluation study

Internet Traffic Engineering : An Artificial Intelligence Approach

Dissertação de Mestrado em Ciência de Computadores, apresentada à Faculdade de Ciências da Universidade do Port

Learning Functions from Imperfect Positive Data

The Bayesian framework of learning from positive noise-free examples derived by Muggleton [12] is extended to learning functional hypotheses from positive examples containing normally distributed noise in the outputs. The method subsumes a type of distance based learning as a special case. We also present an effective method of outlier-identification which may significantly improve the predictive accuracy of the final multi-clause hypothesis if it is constructed by a clause-by-clause covering algorithm as e.g. in Progol or Aleph. Our method is implemented in Aleph and tested on two experiments, one of which concerns numeric functions while the other treats non-numeric discrete data where the normal distribution is taken as an approximation of the discrete distribution of noise