The work laid out here allows you to get extra credit. It is designed to impress upon you the fact that there is high-quality research and writing in books and journals. Most of the material you can easily find on the web is not of high quality and it would be a mistake and a waste of time for you to pay much attention to it.
Here is the reasoning behind what I have just said: Papers that are submitted to professional conferences and to technical journals are reviewed by experts. Only the high quality ones are accepted and published. Anyone can write whatever they want and post it on the web. There is no guarantee that it is of any quality. Most of the material published on the web, if submitted to a high quality journal or conference, would be rejected, because the results are already known, the writing is obscure, or just plain wrong. This Extra Credit page guides you toward high-quality research in artificial intelligence. Learning to sort out the wheat from the chaff is useful whenever you use the web, and useful in every aspect of your life.
This exercise is an experiment. I have not made such an exercise available in past courses. I would estimate that one, or especially two instances of this assignment, if well-done, would raise your semester grade by half a grade, e.g., from B+ to A-. More importantly, the work you do on this exercise will increase your knowledge of AI and help you in your other work in the course, possibly raising your grade evenmore.
For this extra credit assignment you are to find and write about high quality AI material. You should write between 250 and 500 words on each item you find. You should not simply repeat what is in the papers you find. You should include your own opinions on what you read, including questions about parts of it you find difficult to understand - the papers can be advanced, and AI is a new area for you. For books, you shouldn't try to write about an entire book. Just choose a modest length section of it to write about, e.g., a ten to twenty page portion of it.
Your search must be confined to books from the class book list I have prepared, or closely related books. These should all be in Snell library. For journals, you must look only at hardcopy journals or e-journals in the Snell library collection as well as papers in proceedings that are typically available electronically in Snell. Your major sources will be journals and meeting proceedings from IEEE and the ACM. You are welcome to get a Consortium card and look at books and journals in other libraries such as MIT's. (By the way: If you are a serious computer science student intending to work in the field, you should join both the ACM and the IEEE Computer Society. Student memberships are not expensive. They'll keep you informed and they'll be good to have on your CV.)
AI is a complex subject, so most of the high-quality papers you'll find will be too advanced to be readily understandable. The way to avoid this problem is to search for papers using the additional keywords "introduction" and/or "tutorial". The best places to look for this material are Google Scholar at http://scholar.google.com/ and/or CiteSeerx at http://citeseerx.ist.psu.edu/. But there is an important requirement related to these sources: You must only use papers you find in this way which are contained in quality journals and conferences. That is, you'll find papers that appeared in high quality journals and conferences some of which are available directly from the web, for free. But if a highly-rated paper is only available through an ACM or IEEE site, you should use Snell Library e-resources to get it there.
Some of the introductory material you'll find, especially on .edu sites, consists of lecture notes, often PowerPoint slides. These are useful adjuncts but should not be the primary material you discuss.