Classics
Extracting refined rules from knowledge-based neural networks
Machine Learning 13 (1), 71-101, 1993.
Hybrid algorithms for constraint satisfaction problems
Computational Intelligence, 9, 268–299.
The complexity of path-based defeasible inheritance
Artificial Intelligence, 62 (2), 303-39.
The Gardens of Learning: A Vision for AI
The field of AI is directed at the fundamental problem of how the mind works; its approach, among other things, is to try to simulate its working -- in bits and pieces. History shows us that mankind has been trying to do this for certainly hundreds of years, but the blooming of current computer technology has sparked an explosion in the research we can now do. The center of AI is the wonderful capacity we call learning, which the field is paying increasing attention to. Learning is difficult and easy, complicated and simple, and most research doesn't look at many aspects of its complexity. However, we in the AI field are starting. Let us now celebrate the efforts of our forebears and rejoice in our own efforts, so that our successors can thrive in their research. This article is the substance, edited and adapted, of the keynote address given at the 1992 annual meeting of the Association for the Advancement of Artificial Intelligence on 14 July in San Jose, California. AI Magazine 14(2): 36-48.
Bayesian analysis in expert systems
Spiegelhalter, D. J., Dawid, A. P., Lauritzen, S., Cowell, R.
The purpose of the Institute of Mathematical Statistics (IMS) is to foster the development and dissemination of the theory and applications of statistics and probability. The Institute was formed at a meeting of interested persons on September 12, 1935, in Ann Arbor, Michigan, as a consequence of the feeling that the theory of statistics would be advanced by the formation of an organization of those persons especially interested in the mathematical aspects of the subject. The Annals of Statistics and The Annals of Probability (which supersede The Annals of Mathematical Statistics), Statistical Science, and The Annals of Applied Probability are the scientific journals of the Institute. These and The IMS Bulletin comprise the official journals of the Institute. The Institute has individual membership and organizational membership.
Tight performance bounds on greedy policies based on imperfect value functions
Williams, R. J. | Baird, L. C. I.
Tech. rep. NU-CCS-93-14, College of Computer Science, Northeastern University.
Machine discovery of effective admissible heuristics
Machine Learning, 12 (1-3), 117-41.
Automatically constructing a dictionary for information extraction tasks
In AAAI-93, pp. 811â816.