zakirox:
yes cr0sh i agree you but could you please post me an better link to know more about AI. even though i search in google i am unable to find a proper one
The thing is, your not going to find much on simply googling "artificial intelligence" - it would be like expecting to learn how to program in C++ by googling "computer science" (not the greatest analogy, but good enough for now I think).
If you really want to learn about artificial intelligence - you'll need a foundation to work from, and you'll need to put in the work. I posted a couple of links to courses via Coursera and Udacity. Take a look at those, and maybe also other coursework those organizations offer. Neither course will be easy (although I found the Coursera ML Class to be easier than the Udacity class) - you will need to have good grounding in statistics, probabilities, and linear algebra to make headway and understanding. Each course takes about 6 to 8 weeks to complete (working a few hours each evening, plus some extra on weekends).
Once you have that understanding, then you can start looking into machine learning concepts outside that scope, plus topics on neural networks and such. I have to also say that the Udacity course, while the more difficult of the two, will give better understanding on how to relate artificial intelligence and machine learning concepts to a robot - since that is the point of the course, to teach you how to "build" a self-driving vehicle - it is after all taught by the guy behind google's self-driving car.
There is so much information out there on such a vast and wide array of topics under the "artificial intelligence" umbrella; many of them relate to robotics, but you have to understand and have that foundation to know how they relate. Also - much information is only available in book form, so you may want to build your library.
Also - you may want to check out this Udacity course as well:
https://www.udacity.com/course/cs271
It was first released as a Stanford class back in 2011 as well (I took it but had to drop out about halfway through due to personal issues). When it was taught then, you had to get this book for the class - which is a great book to learn from (it's a textbook, though - so realize that if you get a recent edition, expect to pay a lot of money):
"Artificial Intelligence: A Modern Approach"
Finally - though these books should be considered "dated" - I like to recommend them; they are mainly an example of something now known as "subsumption architecture". At the time (late 1970s - early 1980s) which was before Rodney Brook's machines - the author was just playing around with some interesting "homebrew" technology of sorts. In the end, his machine that he named "Rodney" became this robot, which was sold for a while for educational purposes:
http://www.rbrobotics.com/Products/RB5X.htm
Anyhow - the author's name is "David L. Heiserman" - his books are:
Build Your Own Working Robot - #841 (ISBN 0-8306-6841-1), HB, © 1976
How to Build Your Own Self-Programming Robot - #1241, (ISBN 0-8306-9760-8), HB, © 1979
Robot Intelligence...with experiments - #1191, (ISBN 0-8306-9685-7), HB, © 1981
How to Design & Build Your Own Custom Robot - #1341, (ISBN 0-8306-9629-6), HB, © 1981
Projects in Machine Intelligence For Your Home Computer - #1391, (ISBN 0-8306-0057-4), HB, © 1982
Build Your Own Working Robot - The Second Generation - #2781, (ISBN 0-8306-1181-9), HB, © 1987
Note that all of these are long out-of-print - if you want to read them, you'll have to purchase them used or find another source. They are very interesting to read, and give a great idea about the "state-of-the-art" in hobbyist robotics for the time period. Virtually all the examples contained in those books could be easily re-created using an Arduino and a simple robotics platform (heck, you could probably do most of it with the Arduino Robot). But again, they shouldn't be thought of as teaching "modern techniques of artificial intelligence" - they need to be looked at and understood with hindsight from today's understanding and knowledge (that's also one of the problems with AI - being able to know what "old stuff" is still relevant, and what isn't).