PHI 133 - Logic, Probability, and Artificial Intelligence
Philosophy 133 offers an introduction to theoretical artificial intelligence, with a focus on nonmonotonic logic, Bayes networks, and machine learning. An intelligent agent acquires knowledge and makes use of it in planning or decision making. This new course focuses more on knowledge acquisition rather than on planning or decision making processes. This course aims to make the core issues in theoretical artificial intelligence accessible to a wider audience meeting minimal prerequisites in logic (PHI 12 and 112) without any background knowledge in computer science or statistics.
Faculty: Hanti Lin
Units: 4
Prerequisites: PHI 12 and 112
Quarters: Varies from year to year; taught in fall quarter 2016
Description
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.
