People in the field still think of these things as being part of it. The latest edition of Norvig's Artificial Intelligence: A Modern Approach[1] is two years o

People in the field still think of these things as being part of it. The latest edition of Norvig's Artificial Intelligence: A Modern Approach[1] is two years old and kicks off with search, CSPs, and first-order logic, for example. Over the years AIMA has kept up with a bunch of stuff that historically has in fact been far more effective than machine learning, like automated planning, which is the basis for (among other things) most video game AI. An all-time great write-up and talk in this area is Three States and a Plan: The A.I. of F.E.A.R.[2]. And then there's linear and non-linear programming and the broader world of operations research, which not only have had more of an impact historically than machine learning but, when you get down to it, are also a useful lens for thinking about machine learning for people who use it but don't really understand it. Training a neural network and solving an MINLP problem are very similar things. 1. https://aima.cs.berkeley.edu/contents.html https://aima.cs.berkeley.edu/contents.html 2. https://alumni.media.mit.edu/~jorkin/gdc2006_orkin_jeff_fear.pdf https://alumni.media.mit.edu/~jorkin/gdc2006_orkin_jeff_fear... and https://www.gdcvault.com/play/1013282/Three-States-and-a-Plan https://www.gdcvault.com/play/1013282/Three-States-and-a-Pla...