Knowledge acquisition from complex domains by combining inductive learning and theory revision

In the process of knowledge acquisition, inductive learning and theory revision play important roles. Inductive learning is used to acquire new knowledge (theories) from training examples; and theory revision improves an initial theory with training examples. A theory preference criterion is critical in the processes of inductive learning and theory revision. A new system called knowar is developed by integrating inductive learning and theory revision. In addition, the theory preference criterion used in knowar is the combination of the MDL-based heuristic and the Laplace estimate. The system can be used to deal with complex problems. Empirical studies have con rmed that knowar leads to substantial improvement of a given initial theory in terms of its predictive accuracy. keywords: knowledge acquisition, inductive logic programming, theory revision, the MDL principle, the Laplace estimate, noisy data.