Concept learning is the induction of a description from a set of examples. Inductive logic programming can be considered a special case of the general notion of concept learning specifically referring to the induction of first-order theories. Both concept learning and inductive logic programming can be seen as a search over all possible sentences in some representation language for sentences th...

The inductive learning problem consists of learning a concept given examples and nonexamples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously learned concepts from the same domain. These learned concepts highlight useful information for other concepts in the domain. We describe ...

Inductive programming—the use of inductive reasoning methods for programming, algorithm design, and software development—is a currently emerging research field. A major subfield is inductive program synthesis, the (semi-)automatic construction of programs from exemplary behavior. Inductive program synthesis is not a unified research field until today but scattered over several different establi...

A growing interest in real-world applications of inductive techniques signi es the need for methodologies for applying them. So far a number of methodologies for applying inductive learning techniques are described. After reviewing several published approaches, a number of unsolved problems are discussed, two major problems being the lack of attention to nontechnical issues and the focus of mos...

Today’s advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce policy distilling algorithm, building on the CN2 rule mining distills into rule-based decision system. At core of our approach is fact an RL process does not just learn policy, mapping from states actions, but also produces extra meta-informat...