PHI 133 - Logic, Probability, and Artificial Intelligence
To understand how knowledge is, or could be, acquired, we must consider two issues: (1) how knowledge could be represented and (2) how to evaluate various learning strategies that are meant to help one acquire knowledge. These two issues are important to anyone who wants to implement a particular knowledge representation and a particular learning strategy in a machine. When addressing issue No. 1, we will cover the two most popular approaches to knowledge representation: non-monotonic logic and Bayesian networks. Issue No. 2 will bring us to one of the most exciting areas in artificial intelligence: machine learning, especially computational/algorithmic/formal learning theory.