Cognitive ontology based upon observability

Given a system an intelligent agent can either completely observe the current state of the system or it can only only observe certain parts of the system. Based upon this idea we can categorize cognition in terms of observability as described by the Hasse diagram below:

The category of reasoning includes all cognitive processes that only involve the mind of the agent and not the external world. One fundamental reasoning process is categorization which can be used for example to produce an ontology of abstract structures such as lists, sets, relations, and numbers. Numeric reasoning can involve not just the set of real numbers but surreal numbers as well.

A fundamental part of abstract reasoning is optimization which is the process of finding an optimal item amongst a set of possibilities based upon some criterion such as in linear programming. This also applies to planning problems in games in which the agent must find the best course of action from the current state to achieve a certain goal.

Often times we cannot simply search the entire game tree which means that we need to use reinforcement learning instead to arrive at the optimal solution to the problem. Unlike with logical deduction the reinforcement learning process only produces approximate results. Another learning process is clustering which allows the agent to create new categories rather then simply working with the established ones.

All of these abstract reasoning processes are fundamentally divorced from the problems of incomplete and inconsistent information that arise from perception. It may be the case that incomplete information is a fundamental property of agents in the physical world as agents can only ever perceive things in their past light cone. Perception include vision, hearing, smell, taste, and touch among other senses. Intelligent agents should be able to learn from all these different sorts of perceptual signals.