"A classic but I'm not quite sure why it was posted?"
and why is it getting so many upvotes? Extending this approach, if anyone is looking for free karma, just post separate links to all the classic CS books, without any comments or added explanation (3 separate submissions for the TAOCP Knuth books ;-)). Lots of karma for the taking ;-)
now that this is on the front page, here is something interesting. There's a 1000 $ prize for designing a cover for the third edition (see top right hand corner of teh page). The cover of the first two editions are very dense with references to AI history. If I could draw worth a damn I would have taken a shot at it.
Serious tease. I expected either the text available online, lecture videos or at the very least a courseware podcast from Berkeley. I guess I'll go back to staring at it at the bookstore...
I actually use a blend of C & Python, C for doing the heavy lifting and Python for the rapid prototyping.
Most AI algorithms have clearly defined segments that are very CPU intensive, by only using C in those areas I get the optimal balance of development speed and execution speed.
Ever since I learnt Cython, this has been my preferred combination as well. I can't overstate enough how great it is to be able to get the rapid prototyping of Python combined with being able to offload the heavy stuff to C.
While the book is good, it lacks the statistical approach to AI and ML. Currently, most of the AI or ML is done in statistical fashion and I think this book does not do justice in introducing those topics well.
Part V Uncertain Knowledge and Reasoning
13 Uncertainty
14 Probabilistic Reasoning
15 Probabilistic Reasoning Over Time
16 Making Simple Decisions
17 Making Complex Decisions
Part VI Learning
18 Learning from Observations
19 Knowledge in Learning
20 Statistical Learning Methods (pdf)
21 Reinforcement Learning
Part VII Communicating, Perceiving, and Acting
22 Communication
23 Probabilistic Language Processing
24 Perception
25 Robotics
Part VIII Conclusions
26 Philosophical Foundations
27 AI: Present and Future
Bibliography (pdf and counts)
Index (html or pdf)
I love this book as well, and I make sure to read it over regularly to spark different ideas - but it's expensive. Buying it was a bitterweet experience, to say the least.
Owell, it's at least the best you're likely to find.
I want to clarify that this is a reference to the TV show Boston Legal, where William Shatner's named partner in a law firm, says his own name frequently, as if to say 'I'm Denny Crane and I'm the best'.
It bored me to tears. The "agent" orientation of the book does not address my needs for AI: data mining and inference. Norvig's companion Paradigms of Artificial Intelligence was a bliss however :-)
The ToC doesn't contain anything about the situated cognition, enactive cognition schools of thought, so I find applying the term AI to this book too broad. The topics covered seems to broadly fall under "automatic problem solving strategies" or something like that. Check out Rodney Brooks' robotics work for anything that feels like intelligence.
Is the Google Code or design-a-cover thing the actual news here?