not bad for an intro: simple math, minimal jargon and pretty organized ( )

I had to read this as part of my Artificial Intelligence course in Georgia Tech's Online Master's in Computer Science program, and as an AI textbook it was excellent. It provides detailed, and easy to follow, algorithms ranging from minimax and alpha-beta to Bayes Nets, Hidden Markov Models, A*, Neural Nets, and plenty more. I did not read every page of this book, but I can attest that I would not have done nearly as well in my course without it and if I need to look up an AI algo, I'll turn here first to read what Russell and Norvig have to say first and then check other resources. ( )

Nice Book to study basics and expertise to AI .

An essential and comprehensive exploration of AI concepts and design. ( )

The now-standard AI textbook, this offers an overview and some detail on classic AI (automated reasoning and theorem proving, planning, and expert systems) as well as an introduction to statistical techniques (machine learning and neural networks, probabilistic and fuzzy models, modern NLP methods). The Situated Agents approach unifies the book loosely, but it doesn't present a grand unified theory of agents, instead emphasizing the bricolage approach common in the real world. The web site, http://aima.cs.berkeley.edu/ , is also an excellent resource. ( )