This paper discusses the bias present in machine learning systems, emphasizing its effect on learnability and complexity. A good bias must allow more concepts to be learned and/or decrease the complexity associated with learning. The paper develops an exhaustive framework for bias, with two important distinctions: \fIstatic\fR versus \fIdynamic\fR and \fIfocus\fR versus \fImagnify\fR. The well-known candidate elimination algorithm (Mitchell) is used to illustrate the framework. Real world learners need dynamic bias. The paper examines two representative systems.
\s+2S\s-2TABB (Utgoff) dynamically magnifies the description space where learning would otherwise be impossible. \s+2E\s-2TAR is a prototype for learning robot assembly tasks from examples--a dynamic focusing mechanism reduces both the real world description space and the task construction complexity. Inductive learning must be viewed as a problem of dynamic search control.