What kinds of problems can AI solve without using a deep neural network? A lot of questions on this site seem to be asking &quot;can I use X to solve Y?&quot;, where X is usually a deep neural network, and Y is often something already addressed by other areas of AI that are less well known?
I have some ideas about this, but am inspired by questions like this one where a fairly wide range of views are expressed, and each answer focuses on just one possible problem domain.
There are some related questions on this stack already, but they are not the same. This question specifically asks what genetic algorithms are good for, whereas I am more interested in having an inventory of problems mapped to possible techniques. This question asks what possible barriers are to AI with a focus on machine learning approaches, but I am interested in what we can do without using deep neural nets, rather than what is difficult in general.
A good answer will be supported with citations to the academic literature, and a brief description of both the problem and the main approaches that are used.
Finally, this question asks what AI can do to solve problems related to climate change. I'm not interested in the ability to address specific application domains. Instead, I want to see a catalog of abstract problems (e.g. having an agent learn to navigate in a new environment; reasoning strategically about how others might act; interpreting emotions), mapped to useful techniques for those problems. That is, &quot;solving chess&quot; isn't a problem, but &quot;determining how to optimally play turn-based games without randomness&quot; is.
I was hoping to see more answers here, but I'll get us started with some examples:

Combinatorial Search Problems: If your problem can be phrased as movement through a combinatorial graph, you don't need a neural network. In particular, your problem should have discrete states, a clear set of actions that are possible in each state, a clear definition of where we start, and a clear definition of what the goal state looks like. The most effective general purpose technique is iterative deepening search. If you have an idea about which moves might be more effective, or better, a function that estimates how far each state is from the goal, you may be able to build a heuristic function and use A* search instead. Common applications for these techniques include pathfinding in video games (or directions in other applications), AI planning, and Automated Theorem Proving.

I'll add some more topics later, but I suspect others have expertise to share here. Let's see some more ideas!