Learning Approximate Control Rules of High Utility

One of the diicult problems in the area of explanation based learning is the utility problem ; learning too many rules of low utility can lead to swamping, or degradation of performance. This paper introduces two new techniques for improving the utility of learned rules. The rst technique is to combine EBL with inductive learning techniques to learn a better set of control rules; the second technique is to use these inductive techniques to learn approximate control rules. The two techniques are synthesized in an algorithm called approximating abductive explanation based learning (AxA-EBL). AxA-EBL is shown to improve substantially over standard EBL in several domains.