Many researchers have noted the importance of combining inductive and analytical learning, yet we still lack combined learning methods that are effective in practice. We present here a learning method that combines explanation-based learning from a previously learned approximate domain theory, together with inductive learning from observations. This method, called explanation-based neural netwo...

Machine learning attempts to build computer programs that improve their performance by automating the acquisition of knowledge from experience. Inductive learning, one of machine learning paradigms, draws inductive inference from a teacher or environment-provided facts. Inductive learning enables the program to identify regularities and patterns in the prior knowledge or training data, and then...

Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductiv...

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 devel...

We study the complexity of invariant inference and its connections to exact concept learning. define a condition on invariants their geometry, called fence condition, which permits applying theoretical results from learning answer open problems in theory. The requires invariant's boundary---the states whose Hamming distance is one---to be backwards reachable bad small number steps. Using this w...

Inductive learning that creates decision trees from a set of existing cases is useful for automated knowledge acquisition. Most of the existing methods in literature are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a fuzzy inductive learning method that integrates the fuzzy set theory into the regular inductive learning processes. The method conver...

This paper suggests that it may be easier to learn several hard tasks at one time than to learn these same tasks separately. In effect, the information provided by the training signal for each task serves as a domain-specific inductive bias for the other tasks. Frequently the world gives us clusters of related tasks to learn. When it does not, it is often straightforward to create additional ta...

Inductive programming systems characteristically exhibit an exponential explosion in search time as one increases the size of the programs to be generated. As a way of overcoming this, we introduce incremental learning, a process in which an inductive programming system automatically modifies its inductive bias towards some domain through solving a sequence of gradually more difficult problems ...