A number of approaches have been advanced for taking data about a user's likes and dislikes and generating a general profile of the user. These profiles can be used to retrieve documents matching user interests; recommend music, movies, or other similar products; or carry out other tasks in a specialized fashion. This article presents a fundamentally new method for generating user profiles that takes advantage of a large-scale database of demographic data. These data are used to generalize user-specified data along the patterns common across the population, including areas not represented in the user's original data. The input data most often take the form of samples of the user's interests or preferences in a given area, and the profile is a generalization of these data that can be used generatively to carry out tasks on behalf of the user.

However, I believe that the distinction of "neats" and "scruffies" raised at Cog Sci in '81 didn't define scruffies as people who built expert systems [they didn't really exist as a "real" part of MAD. Instead, I believe AI These are the researchers who read Hawkings and say "gee, if his model of the lo-23 second big bang is right, then the distribution of intergalactic gases should be relatively even. Let's go see if that's true. However, to run our experiments we'll need a more sensitive space-based sensing device, so let's work with the engineers to design one." I think one could make the case (although not from the data collected in Cohen's survey) that the two methodologies are not informed and influenced by each other to the extent they should or could be.

A large body of work exists today in the area of design, including Brown and Chandrasekharan (1989) and Dym and Levitt (1991). Also see the Winter 1990 issue of AI Magazine, which was guest edited by J. S. Gero with Mary Lou Maher. By virtue of the teaching experience that supports this book, however, it is more comprehensive and presents its viewpoint in a systematic, orderly progression. The differences between the approaches by different groups are slight. For example, the propose, critique, and modify approach of Chandrasekaran's group (AI Magazine, Winter 1990) is akin to the prototype creation, refinement, and adaptation idea previously described.

It is impossible for a travel agent to keep track of all the offered tour packages. Traditional database-driven applications, as used by most of the tour operators, are not sufficient enough to implement a sales process with consultation on the World Wide Web. The last-minute travel application presented here uses case-based reasoning to bridge this gap and simulate the sales assistance of a human travel agent. A case retrieval net, as an internal data structure, proved to be efficient in handling the large amount of data. A usual tour package contains the flight to the destination and back, transfers from the airport to the hotel and back, board, and lodging.

His proofs are ingenious, cleverly argued, quite convincing to many of his contemporaries, and utterly wrong. The Simon Newcomb Award is given annually for the silliest published argument attacking AI. Our subject may be unique in the virulence and frequency with which it is attacked, both in the popular media and among the cultured intelligentsia. Recent articles have argued that the very idea of AI reflects a cancer in the heart of our culture and have proven (yet again) that it is impossible. While many of these attacks are cited widely, most of them are ridiculous to anyone with an appropriate technical education.

Although today's world offers us unprecedented access to greater and greater amounts of electronic information, we are faced with significant problems when it comes to finding the right information at the right time--the essence of the information-overload problem. One of the proposed solutions to this problem is to develop technologies for automatically learning about the implicit and explicit preferences of individual users to customize and personalize the search for relevant information. For example, modern search engines provide only a first cut through the information space, leaving the user with a significant search task to locate individual information items. This information overload is beginning to cause problems on the internet and is seen as a serious barrier to its future success. This problem takes on even more significance when one considers the new generation of mobile phones, which offer users an alternative internet access route through the wireless application protocol (WAP).

And, "If Cohen is taken (overly) seriously, then every major MAD will prove generally useful. The authors relied heavily on the analysis of metaphor proffered by Earl MacCormic, and his distinction between epiphor and diaphor is useful in the analysis of metaphor in science. Readers of AI Magazine should be apprised, however, of other works on metaphor which may help clarify some of the issues. For one thing, there exists a considerable literature of experimental research on metaphor comprehension which bears on the claim that metaphors can be described as similarity statements. Suffice it to say that the scaling of the fruitfulness of metaphors by means of simple metrics of the features that are shared by the terms in a metaphor is not so straightforward as West and Travis imply Furthermore, the distinction between diaphor and epiphor is rather slippery, and the slipperiness is compounded by West and Travis' additional concept of "paraphor."

Case-based reasoning (CBR) is a problem-solving paradigm that uses exemplars or previous solutions to solve new problems (Aamodt and Plaza 1994; Kolodner 1993). First, CBR can reduce search. Solution reuse is compatible with a wide range of problem-solving methods, so a CBR component can be used in many types of problem-solving system. When similar problems recur, CBR can significantly improve performance. This performance improvement can be particularly significant for problems with large search spaces, such as planning and design (Bergmann and Wilke 1996; Branting and Aha 1995; Veloso 1994; Koton 1988).

Having convenient access to expert knowledge is important. In the past, we have seen users reinvent solutions because they did not have access to previous experience on the same fault. This lack of available information has led to wasted resources and, in some cases, has generated responses to the fleet that were not accurate enough. The development began in fiscal year 1992, and the area between the solid and dotted lines approximates the cost for development. The peak in fiscal year 1994 represents the end of the operational evaluation and the beginning of production operation.

This issue marks the end of volume 12 and my last issue as editor in chief. In 1981 when Lee Erman (then chairman of the Publications Committee) asked me to take over the editorship of a fledgling magazine, I assumed it would be a Z-or 3-year responsibility. I never expected to still be doing it 10 years later. I also never expected it to be such a pleasurable avocation. The pleasure comes from several directions: sitting in an honored position to observe developments in AI all over the world by being the recipient of dozens of papers every year; working with an excellent supporting staff of coeditors, assistants, and production people; and receiving compliments from colleagues for doing a good job.