reiter@babbage.harvard.edu (Ehud Reiter) (05/17/88)
About a month ago, I posted a note asking if any "exciting" work existed in AI which: 1) Was highly thought of by at least 50% of AI researchers. 2) Was a positive contribution, not an analysis showing problems in previous work. 3) Was in AI as narrowly defined (i.e. not in robotics or vision) Well, I'm still looking. I have received some suggestions, but almost all of them have seemed problematical. The most promising were Spencer Star's suggestions for exciting work in machine learning (published in a previous AIList, including Valiant's theoretical analyses, Quinlan's decision trees, and explanation-based learning). However, after looking at some books and course syllabi in machine learning, I was forced to conclude that the topics mentioned by Spencer did not satisfy condition (1), as the topics he mentioned had very little overlap with the topics in the books and syllabi (which, incidentally, had very little overlap with each other). So, I'm still looking for work which meets the above criteria, and hoping to thereby convince my friend that there is some cohesion to AI. If anyone has suggestions, please send them to me! Ehud Reiter reiter@harvard (ARPA,BITNET,UUCP) reiter@harvard.harvard.EDU (new ARPA)