Perception and reasoning

After thinking about the different branches of AI I now feel that the most important basic distinction to make is between perception and reasoning. This is similar to the distinction between empiricism and rationalism in epistemology.

An intelligent agent that isn't dealing with an unknown environment such as one that is playing a game such as go, chess, checkers, or tictactoe in which there is no fog of war has no need for perception as we know it. Likewise intelligent agents that are solving logical puzzles such as sudoku only need to use their reasoning.

When an intelligent agent needs to understand the real world it ends up producing an empirical knowledge base which includes facts such as that Einstein once lived from 1879 to 1955 and patterns in the spacetime environment such as Newton's universal law of gravitation. Every single object in the empirical knowledge base is uncertain to some probability and dependent on the current point in time.

One of the fundamental things about the reasoning / perception distinction is that the aforementioned empirical knowledge base can be defined as a removable part. An agent capable of intelligent reasoning should be able to learn everything about the world from scratch based upon its learning and reasoning capabilities alone.

Likewise, an intelligent agent that know nothing about the real world could still play chess intelligently or produce logical solutions to sudoku. The reasoning component in general is very much mathematical / logical in nature as it focuses on mental objects that may have no physical instantiation.

One possible alternative to the reasoning / perception distinction is the declarative / procedural distinction, however, I don't think this is really valuable because there is no way we can really remove a procedural component from an AI as the techniques of optimization and decision theory are too fundamentally intertwined with the rest of the agent's reasoning capabilities to really be removable.