In this paper, we discuss the spectrum of on-line assistance ranging from passive, canned to active, user-customized. We discuss various of on-line assistance: interactive introductory tutorials, on-line help, and on-line manuals. We then describe two steps to make on-line assistance more intelligent: (1) inclusion and customization of examples in the information provided the user; and (2) integration of various aspects of on-line assistance like tutorials and help.

In this paper, we present three examples of the development of legal concepts and rules analyze them in therms of AI learning methods, in particular, the candidate elimination algorithm. The three areas of legal doctrine are: (1) the doctrine of the liability of manufacturers to third party buyers; (2) the Fourth Amendment doctrine relating to stop-and-frisk searches; and (3) the doctrine of attractive nuisance. For each of the legal areas, we give a synopsis of the legal developments and show how it can be viewed from an Ai point of view.