Knowledge-based learning: Integration of deductive and inductive learning for knowledge base completion

To learn effectively, a system needs to use all the knowledge that is available. Explanation-based learning and similarity-based learning operate over a domain theory and a set of examples, respectively, but neither approach makes extensive use of both forms of knowledge. Many problems in engineering and other areas can provide a learning system with an incomplete domain theory and a limited set of examples. Knowledge-based learning uses knowledge in both forms to learn knowledge missing from the domain theory.The knowledge-based learning approach is illustrated with two systems, KBL0 and KBL1. These systems have been designed and implemented to work with domains requiring a representation of real numbers and mathematical formulas, such as engineering.This research has shown, not only that it is possible to use a domain theory to guide induction using examples, but that when there are few examples available compared to the size of the problem space, the resulting rules are more accurate and stable than those from pure empirical techniques. In addition, knowledge-based learning algorithms free the user from selecting relevant examples and attributes for learning by using an incomplete domain theory to determine where knowledge needs to be added. A problem unsolved by the current domain knowledge helps to determine where new knowledge needs to be incorporated into the domain theory and what the context is for the learning. The context is used to select relevant examples from an example base and to reduce the number of attributes used during induction. With the control structure provided by knowledge-based systems, inductive learning can be used to extend an existing knowledge base.U of I OnlyETDs are only available to UIUC Users without author permissio