A great many AI problems can be phrased simply as a search through a domain of elements for one that meets criteria C. The planning problem is certainly this way. As a simple example, suppose one has an environment (an MDP, for some generality), with finite horizon:
Given this formulation, there are finitely many policies to examine. The naive solution would be to enumerate them, see what reward each one got you, and then take the policy that gave you the biggest reward. Of course, we don’t do this because, for any remotely realistic domain, this search space is so huge as to make this process intractable. To actually solve this problem, people developed Value Iteration, or Linear Programming or what have you. These methods allow you to efficiently cut through the giant search space.
The interesting thing is, 15, 20, or 25 years from now, that search space won’t seem nearly as large as it seems to us today. Computers will be much, much faster, and can simply plow through that enormous search space in the blink of an eye. No sophisticated methods of cutting through the space such as Value Iteration (VI) will be necessary. Using VI and using brute force search will be the difference between .20 and .25 milliseconds.
The question is, then, if the constant advance of computer technology will eventually make planning methods totally irrelevant.