[shagie]

Would those topics that "outside academia understands AI to include" be covered in http://aima.cs.berkeley.edu ?

When you say "bots in video games as AI" that's covered in the book titled Artificial Intelligence: A Modern Approach, 4th US ed. :

    II Problem-solving 
        3 Solving Problems by Searching    ...  63 
        4 Search in Complex Environments   ... 110 
        5 Adversarial Search and Games     ... 146 
        6 Constraint Satisfaction Problems ... 180 

Those topics would be in chapter 5.

Sure, it may be a few hundred lines of code, but it's still something that a Berkley written AI textbook covers.

Spelled out more for that section:

    Chapter 5   Adversarial Search and Games ... 146

    5.1   Game Theory ... 146 
        5.1.1   Two-player zero-sum games ... 147 
    5.2   Optimal Decisions in Games ... 148 
        5.2.1   The minimax search algorithm ... 149 
        5.2.2   Optimal decisions in multiplayer games ... 151 
        5.2.3   Alpha--Beta Pruning ... 152 
        5.2.4   Move ordering ... 153 
    5.3   Heuristic Alpha--Beta Tree Search ... 156 
        5.3.1   Evaluation functions ... 156 
        5.3.2   Cutting off search ... 158 
        5.3.3   Forward pruning ... 159 
        5.3.4   Search versus lookup ... 160 
    5.4   Monte Carlo Tree Search ... 161 
    5.5   Stochastic Games ... 164 
        5.5.1   Evaluation functions for games of chance ... 166 
    5.6   Partially Observable Games ... 168 
        5.6.1   Kriegspiel: Partially observable chess ... 168 
        5.6.2   Card games ... 171 
    5.7   Limitations of Game Search Algorithms ... 173

[Animats]

I think I have an original edition of that book somewhere. Good Old Fashioned AI.

[shagie]

My assignments (different book) for Intro to AI class were:

Boolean algebra simplifier. Given a LISP expression - for example (AND A (OR C D)) write a function to return the variables needed to make the entire expression TRUE. Return NIL if the expression is a paradox such as (AND A (NOT A)). The expressions that we were to resolve had on the order of 100-200 operators and were deeply nested. I recall that I wrote a function as part of it that I called HAMLET-P that identified terms of the form (OR 2B (NOT 2B)) and rapidly simplified them to TRUE.

Not-brute-force job scheduler. The job-shop scheduling problem ( https://en.wikipedia.org/wiki/Job-shop_scheduling ) with in order processing of multiple tasks that had dependencies. Any worker could do any task but could only do one task at a time.

The third one I don't remember what it was. I know it was there since the class had four assignments... (digging... must have been something with Prolog)

The last assignment was written in any language (I did it in C++ having had enough of LISP and I had a good model for how to do it in my head in C++). A 19,19,5 game ( https://en.wikipedia.org/wiki/M,n,k-game ). Similar to go-maku or pente. This didn't have any constraints that go-maku has or captures that pente has. It was to use a two ply min-max tree with alpha beta pruning. It would beat me 7 out of 10 times. I could get a draw 2 out of 10 and win 1 out of 10. For fun I also learned ncurses and made it so that I could play the game with the arrow keys rather than as '10,9... oh crap, I meant 9,10'.

And I still consider all of those problems and homework assignments as "AI".

From the digging, I found a later year of the class that I took. They added a bit of neural nets in it, but other topics were still there.

By way of https://web.archive.org/web/19970214064228/http://www.cs.wis... to the professors's home page and classes taught - https://web.archive.org/web/19970224221107/http://www.cs.wis...

Professor Dryer taught a different section https://web.archive.org/web/19970508190550/http://www.cs.wis...

The domain of the AI research group at that time: https://web.archive.org/web/19970508113626/http://www.cs.wis...