This paper reviews Machine Learning (ML), and extends and complements previous work
(Kocabas, 1991; Kalkanis and Conroy, 1991). Although this paper focuses on inductive …

Explanation-based learning (EBL) is a technique by which an intelligent system can learn by
observing examples. EBL systems are characterized by the ability to create justified …

The utility problem occurs when the cost associated with searching for relevant knowledge
outweighs the benefit of applying this knowledge. One common machine learning strategy …

As case-based reasoning systems are deployed in real-world situations the issue of case
maintenance becomes more and more critical. Uncontrolled case-base growth can cause …

Proper indexing of cases is critically important to the functioning of a case-based reasoner.
In real domains such as fault recovery, a body of domain knowledge exists that can be …

Operationality is the key property that distinguishes the final description learned in an
explanation-based system from the initial concept description input to the system. Yet most …

Derivational analogy solves a problem by replaying the plan used to solve a previous
problem, modifying it where necessary. We analyze how four published systems use this …

Abstract Knowledge has traditionally been considered to have a beneficial effect on the
performance of problem solvers but recent studies indicate that knowledge acquisition is not …

This book presents a theory of learning new causal relationships by making use of
perceived regularities in the environment, general knowledge of causality, and existing …

Since researchers in each of the many various subfields of machine learning are often
unaware of related work done within the other subfields, this book brings together many of …