Book review: Empirical Methods for Artificial Intelligence by Paul R. Cohen (The MIT Press, 1995)

Authors:
Robert C. Holte
Affiliations:
Computer Science Dept. University of Ottawa Ottawa, Ontario CANADA KiN 6N5 email: holte@csi.uottawa.ca
Venue:
ACM SIGART Bulletin
Year:
1996

Citing 5
Cited 0

Trial by fire: understanding the design requirements for agents in complex environments

AI Magazine
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
Extracting viewpoints from knowledge bases

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
UMass/Hughes: description of the CIRCUS system used for MUC-5

MUC5 '93 Proceedings of the 5th conference on Message understanding
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)

Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This book fills a long-standing void on the bookshelves of AI researchers. It provides practical guidelines on how to conduct empirical research in AI. Although I've been doing experimental AI for almost a decade, I learned a great deal from Cohen's book and I expect I'll refer to it often in the coming years.