Empirical Methods for Artificial Intelligence (Bradford Books) [Hardcover]

Paul R. Cohen (Author)

Book Description

Publication Date: August 3, 1995 | ISBN-10: 0262032252 | ISBN-13: 978-0262032254 | Edition: First Edition

Computer science and artificial intelligence in particular have no curriculum in research methods, as other sciences do. This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data. Although many of these techniques are statistical, the book discusses statistics in the context of the broader empirical enterprise. The first three chapters introduce empirical questions, exploratory data analysis, and experiment design. The blunt interrogation of statistical hypothesis testing is postponed until chapters 4 and 5, which present classical parametric methods and computer-intensive (Monte Carlo) resampling methods, respectively. This is one of few books to present these new, flexible resampling techniques in an accurate, accessible manner.Much of the book is devoted to research strategies and tactics, introducing new methods in the context of case studies. Chapter 6 covers performance assessment, chapter 7 shows how to identify interactions and dependencies among several factors that explain performance, and chapter 8 discusses predictive models of programs, including causal models. The final chapter asks what counts as a theory in AI, and how empirical methods -- which deal with specific systems -- can foster general theories.Mathematical details are confined to appendixes and no prior knowledge of statistics or probability theory is assumed. All of the examples can be analyzed by hand or with commercially available statistics packages.The Common Lisp Analytical Statistics Package (CLASP), developed in the author's laboratory for Unix and Macintosh computers, is available from The MIT Press.A Bradford Book

Editorial Reviews

About the Author

Paul Cohen is Professor in the Department of Computer Science at the University of Massachusetts at Amherst.

Product Details

• Hardcover: 421 pages
• Publisher: A Bradford Book; First Edition edition (August 3, 1995)
• Language: English
• ISBN-10: 0262032252
• ISBN-13: 978-0262032254
• Product Dimensions: 9.3 x 8.2 x 1.1 inches
• Shipping Weight: 2.2 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,128,549 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

8 of 8 people found the following review helpful:

5.0 out of 5 stars Excellent introduction to experimental science, April 9, 2003

By 

Mihailo Despotovic (Silicon Valley, CA USA) - See all my reviews
(REAL NAME)   

This review is from: Empirical Methods for Artificial Intelligence (Bradford Books) (Hardcover)

The title of the book could have been easily "Empirical Methods for Computer Science" or even "Empirical Methods for Science."

The goal of the book is to give a gentle but solid introduction into empirical research, experimental science and interpretation of data.

First four chapters are really a must-read for anyone who is interested in empirical methods. In the first chapter "Empirical Research", the author lays the foundations. Chapter two "Exploratory Data Analysis" starts with the fundamentals of statistics of one variable and introduces time series and execution traces. I really loved the "Fitting functions to Data in Scatterplots" subchapter. The introduction continues in the third chapter "Basic Issues in Experimental Design" where we learn about control, spurious effects, sampling bias, dependent variables and pilot experiments. The author gives some nice advices here. Fourth chapter is "Hypothesis Testing and Estimation" and this one concludes the introductory part.

Chapters 5-9 are a little bit more advanced and somewhat biased towards Computer Science and Artificial Intelligence but could be an interesting and refreshing read to anyone who wants to get a solid foundation to experiment design, execution, data collection and interpretation.

The author uses experimental data generated by a system called "Phoenix" (which he codeveloped) as the main example in the book.