Knowledge Programming in Loops: Report on an Experimental Course.
Mark Stefik, Daniel G. Bobrow, Sanjay Mittal, Lynn Conway · American Association for Artificial Intelligence eBooks · 1983
Early this year fifty people took an experimental course at Xerox PARC on knowledge programming in Loops During the course, they extended and debugged small knowledge systems in a simulated economics domain called Truckin Everyone learned how to use the Loops environment, formulated the knowledge for their own program, and represented it in Loops At the end of the course a knowledge competition was run so that the strategies used in the different systems could be compared The punchline to this story is that almost everyone learned enough about Loops to complete a small knowledge system in only three days. Although one must exercise caution in extrapolating from small experiments, the results suggest that there is substantial power in integrating multiple programming paradigms. KNOWLEDGE PROGRAMMING is concerned with the techniques for representing knowledge in computer programs. It is important in many applications of AI, where the problems ‘Now with the Defense Advanced Research Projects Agency (DARPA). Copyright @ 1983 by Xerox Corporation Thanks to Johan de I<leer, Richard Fikes and John McDermott for their reviews and comments on earlier drafts of this paper. We extend our special thanks to the course participants from Applied Expert Systems, Daisy Systems, ESL, Fairchild AI Lab, Lawrence-Livermore Laboratories, Schlumberger-Doll Research Laboratory, SRI International, Stanford University, Teknowledge, and Xerox Corporation Their participation and feedback are vital to the ongoing experimental process for simplifying the techniques of knowledge programming We enjoyed and will long remember their spirited involvement. are messy. As in many situations in life, pat solutions and simple mathematical models just aren’t good enough. Things break. Information is missing. Assumptions fail. Situations are complicated. To cope with messiness, AI researchers have found that large amounts of problem-specific knowledge are usually needed. This places a premium on the use of powerful techniques for representing and testing knowledge in computer programs. Very few people have been trained to build knowledge systems. This is a critical bottleneck that limits the scope and impact of knowledge engineering. It limits the number of things that can be tried, the number of good ideas that are propagated, and the number of successful applications that influence the way that others perceive the field. A few numbers may serve to put this in perspective. About one computer science researcher in ten does some work in AI, and perhaps a fifth of those work in knowledge engineering. In 1980, approximately 265 people graduated with Ph.D.‘s in Computer Science, according to the “Snowbird Report” (Denning, et al., 1981). Fewer than a half dozen doctoral theses appear each year on some aspect of building knowledge systems. An estimate in a brochure by Teknowledge, Inc., indicates that there are only about sixty people in the world with high level expertise in the design and development of knowledge systems. Although precise figures for these populations are difficult to obtain, all the evidence suggests that the community is tiny, indeed. THE AI MAGAZINE Fall 1983 3 AI Magazine Volume 4 Number 3 (1983) (© AAAI)