Posts Tagged ‘artificial intelligence’

Talking about Probabilistic Robotics

September 23, 2007

Sebastian Thrun is a professor of computer science and electrical engineering at Stanford, and director of the Stanford Artificial Intelligence Laboratory. He was the leader of Stanford’s team which won the $2 million first prize in the 2005 DARPA Grand Challenge, which was a race of driver-less robotic cars across the desert, and also leads Stanford’s entry into the 2007 DARPA Urban Challenge.

One of the ingredients in the Stanford team’s win was their use of “probabilistic robotics,” which is an approach based on the recognition that all sensor readings and models of the world are inherently subject to uncertainty and noise. Thrun, together with Wolfram Burgard and Dieter Fox have written the definitive text on probabilistic robotics, which will be a standard for years to come. If you are seriously interested in robotics, you should read this book. (The introductory first chapter, which clearly explains the basic ideas of probabilistic robotics is available as a download here.)

The Laboratory of Intelligent Systems at the Swiss École Polytechnique Fédérale de Lausanne (EPFL) hosts the superb “Talking Robots” web-site, which consists of a series of podcast interviews with leading robotics researchers. I noticed that the latest interview is with Thrun, and liked it quite a bit; it is well worth downloading to your iPod or computer.

You can watch Thrun speaking about the DARPA Grand Challenge at this Google TechTalk.

Artificial Intelligence: A Modern Approach

September 20, 2007

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“Artificial Intelligence: A Modern Approach,” by Stuart Russell (professor of computer science at UC Berkeley) and Peter Norvig (head of research at Google) is the best-known and most-used textbook about artificial intelligence, and for good reason; it’s a great book! The first edition of this book was my guide to the field when I was switching over from physics research to computer science.

I feel almost embarrassed to recommend it, because I suspect nearly everybody interested in AI already knows about it. So I’m going to tell you about a couple related resources that are maybe not as well-known.

First, there is the online code repository to the algorithms in the book, in Java, Python, and Lisp. Many of the algorithms are useful beyond AI, so you may find for example that the search or optimization algorithm that you are interested in has already been written for you. I personally have used the Python code, and it’s really model code from which you can learn good programming style.

Second, if you haven’t ever visited Peter Norvig’s web-site, you really should. I particularly recommend his essays “Teach Yourself Programming in Ten Years,” “Solving Every Sudoku Puzzle,” and “The Gettysburg Powerpoint Presentation.”