Courses at Stanford for Logical AI People

Classes in mathematics I believe are the most fundamental for anyone interested in logical AI. To formalize common sense reasoning, you need to be able to use mathematics to formalize things, and know some common sense. Most people already have common sense, although I must admit many academics like myself are a little deficient in that area. Hence all you need to learn is how to represent concepts in mathematics.

Math and Logic

1. Phil 160A: Logic, ?.
2. CS 157: Logic and Automated Reasoning , Mike Genesereth.
3. CS 323: Algebraic Logic, Vaughan Pratt. More algebra.
4. Math290A/B: Model Theory, Solomon Feferman. A must for logical AI people.
5. Math 291A/B: Recursion Theory, Solomon Feferman. Good for mathematical rigor and concepts, introduction to notions of computability. Math291B does recursion theory for reals.
6. Math292A/B: Set Theory, Solomon Feferman. Very good for seeing how to build structure, prove theorems, etc.

1. CS 221: Artificial Intelligence, Daphne Koller. A great introduction to the newest concepts of AI. Includes search, bayes nets, neural nets, a little logic, MDPs, game trees, CSPs, planning, satisfiability, decision trees.
2. CS 323: Commonsense Reasoning, McCarthy/Costello. An introduction to many of John McCarthy's papers, including circumscription, philosophical presuppositions of logical AI, elaboration tolerance, and context. We also explore other fundamental parts of logical AI, including a brief introduction to logic and model theory, and the frame problem.
3. Phil 169: Intensional Logic, Johan van Benthem. Modal logic, all sorts of good stuff. van Benthem is a great teacher!
4. PHIL 298: Logical Dynamics Johan van Benthem. This course turns logic into an intriguing toy. van Benthem transforms the mundane task of proving a formula into an engaging argument, or even more exciting, a game played against another person who is constantly trying to prove you wrong! This course is a lot of fun.
5. CS 228: Reasoning Under Uncertainty, Daphne Koller. Learn about Bayes Nets, representational as well as computational problems. How to represent causality. Even though this isn't logical AI persay, it is good to know about other approaches to codifying knowledge. Even though Bayes Nets are built on top of probability distributions, there is still a lot of formalization that is good to know and see.

1. CS 528: Broad Area Colloquium for Artificial Intelligence, Geometry, Graphics, Robotics and Vision
2. Nobots.
3. Logic Seminar (offered through the math department)
4. Logic Lunch (offered through the math department)

1. CS 051: Introduction to Quantum Computing, Colin Williams. Fun and cool! (Especially if you understand and appreciate quantum physics.)
2. CS 255: Cryptography, Dan Boneh. Beautiful applications of number theory to get cryptographic solutions. Cryptography is related to logical AI in that we are both using logic/math to built objects that satisfy certain properties. (They want functions for which it is difficult to find a collision, we want formalisms that can handle the frame problem elegantly.)
3. Golf: Golf, Jim Miller. A fun sport to learn. Also a great way to get a tan.

1. CS 222: Knowledge Representation, Richard Fikes.
2. CS 227: Reasoning Methods in AI, Pandurang Nayak.
3. LING 230A/B : Semantics and Pragmatics -- plan to take this quarter
4. Phil 160B L Computability and Logic Grisha Mints. Spring 2001.

1. Dan Boneh
2. Solomon Feferman
3. Mike Genesereth
4. Daphne Koller
5. John McCarthy
6. Grigori Mints
7. Vaughan Pratt
8. Colin Williams
9. Johan van Benthem