Learning to Explain with ABML

The main advantage of machine learning algorithms that learn simple symbolic models is in their capability to trivially provide justifications for their decisions. However, there is no guarantee that these justifications will be understood by experts and other users. Induced models are often strange to the domain experts as they understand the problem in a different way. We suggest the use of argument based machine learning (ABML) to deal with this problem. This approach combines machine learning with explanations provided by domain experts. An ABML method is required to learn a model that correctly predicts learning examples and is consistent with the provided explanations. The present paper describes an application of ABML to learning a complex chess concept of an attack on the castled king. The explanation power of the learned model for this concept is especially important, as it will be used in a chess tutoring application.