I understood that searching is important in AI.There's aquestion在这个网站上关于这个话题,但也可以直观地理解为什么。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 theory behind AI (I've heard about "agents"),but what I actually learned was basically a few searching algorithms,like:
- Uniform-cost search
- Iterative-deepening search
- Bidirectional search
these searching algorithms are usually categorised 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" searching algorithms (specifically applied to TSP problem).These algorithms are either constructive (e.g.,NN),local search (e.g.,2-opt) algorithms or meta-heuristic ones (e.g.,ACS,SA,etc).
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 didn't remain with the feeling that AI is more than searching,either "stupidily" or "more intelligently".
My 必威英雄联盟questions are:
Why would one professor only teach searching 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 必威电竞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 theories behind AI that could be taught in this kind of course?