Inductive Logic Programming
Inductive Logic Programming (ILP) is a machine learning algorithm in the field of Artificial Intelligence that seeks to mimic intuition through inductive reasoning based on empirical data gained from experiences. This approach to implementing intelligent application is beneficial in the area of molecular biology and natural language which has managed well with the problem of redundancy of natural expressive languages. The method of proving whether a hypothesis is true or false through the examination of each possible predicate and determining its absolute probability of being true or false is superimposed until every predicate concludes the hypothesis. Inductive Logic Programming utilizes theorem from computational linguistics to further exploit the complexity of the facet of natural languages. Deciphering meaning through the understanding of the many variables can give meaning. Exploring first-order logic from multi-relational data, quantifiable variables, inferable rules on certain data for background facts to draw conclusive outcome of an analysis, such a technique is not infallible but performs better than any other algorithm that tackle complex system to analyze.