Computer Science Syllabus - Artificial Intelligence II
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Lecturer: Dr S.B. Holden
No. of lectures and examples classes: 12 + 4
Prerequisite courses: Artificial Intelligence I, Logic and Proof, Continuous Mathematics (Mathematical Methods for Computer Science from 2006), Discrete Mathematics, Probability
The aim of this course is to build on Artificial Intelligence I, first by introducing more elaborate methods for knowledge representation and planning within the symbolic tradition, but then by moving beyond the purely symbolic view of AI and presenting methods developed for dealing with the critical concept of uncertainty. The central tool used to achieve the latter is probability theory. The course continues to exploit the primarily algorithmic and computer science-centric perspective that informed Artificial Intelligence I.
The course aims to provide further tools and algorithms required to produce AI systems able to exhibit limited human-like abilities, with an emphasis on the need to obtain richer forms of knowledge representation, better planning algorithms, and systems able to deal with the uncertainty inherent in the environments that most real agents might be expected to perform within.
At the end of this course students should
* Russell, S. & Norvig, P. (2003). Artificial intelligence: a modern approach. Prentice-Hall (2nd ed.).
Next: Computer Systems Modelling Up: Michaelmas Term 2005: Part Previous: Michaelmas Term 2005: Part   Contents Christine Northeast
Sun Sep 11 15:46:50 BST 2005