I understood that the concept of search is important in AI. There's a question on this website regarding this topic, but one could also intuitively understand why. I've had an introductory course on AI, which lasted half of a semester, so of course there wasn't time enough to cover all topics of AI, but I was expecting to learn some AI theory (I've heard about "agents"), but what I actually learned was basically a few search algorithms, like:
- Uniform-cost search
- Iterative-deepening search
- Bidirectional search
these search algorithms are usually categorized as "blind" (or "uninformed"), because they do not consider any information regarding the remaining path to the goal.
Or algorithms like:
- Heuristic search
- Best-first search
which usually fall under the category of "informed" search algorithms, because they use some information (i.e. "heuristics" or "estimates") about the remaining path to the goal.
Then we also learned "advanced" search algorithms (specifically applied to the TSP problem). These algorithms are either constructive (e.g., nearest neighbor), local search (e.g., 2-opt) algorithms or meta-heuristic ones (e.g., ant colony system or simulated annealing).
We also studied briefly a min-max algorithm applied to games and an "improved" version of the min-max, i.e. the alpha-beta pruning.
After this course, I had the feeling that AI is just about searching, either "stupidly" or "more intelligently".
My questions are:
Why would one professor only teach search algorithms in AI course? What are the advantages/disadvantages? The next question is very related to this.
What's more than "searching" in AI that could be taught in an introductory course? This question may lead to subjective answers, but I'm actually asking in the context of a person trying to understand what AI really is and what topics does it really cover. Apparently and unfortunately, after reading around, it seems that this would still be subjective.
Are there AI theories that could be taught in this kind of course?