I have a theory on AI that I would like to write a "whitepaper" about. The distinction I want to explore in AI is learning vs. strategizing. My question is, where can I read other material about this subject?
Let me give a chess example. Let's look at a chess AI as a max-tree, where capturing an enemy unit adds that unit's value to the "move score" for that decision (and likewise losing a piece subtracts that value to the score). Capturing a pawn might net 1 point, a knight 4 points, a rook 5 points, etc.
Strategizing would be AI to apply these points and determine the next move; eg. given ten possible moves, pick the best (max score) at the end of three moves.
Learning would be applying statistical observation to determine those values. If you play 100 games, the AI might decide that capturing a pawn is 2 points, and a knight is worth 7 points, while a rook is only worth 3 points (based on 100 gameplays).
Does this distinction already exist in literature, and if so, where can I read about it?
Edit: Does anyone know a Chess game (with source-code preferably) that utilizes this approach? Maybe Chess960@Home?