The proof theory and semantics of operator-free simulation language

Humans can at least apparently conduct deductive reasoning. Various logical inference systems have been developed in order to characterize such human reasoning. The strength of such a properly logical analysis of human reasoning lies in its productivity and predictive power; it enables the theorist to define precisely which data-space the theory covers and then mathematically prove that the theory covers the intended data-space sound and complete. However, as a demerit of a properly logical approach to human reasoning, it is not clear how humans have developed the ability to deal with the knowledge representations that such an analysis indicates. The structure of the logical language representations, which includes abstract elements such as quantifiers and disjunction operators, deviates greatly from the structure of the representations that the other cognitive mechanisms (such as vision and auditory perception) deal with. Thus, a properly logical analysis of human reasoning implies a larger evolutionary gap in the development of this cognitive ability. Moreover, the presence of quantifiers and other propositional operators in the logical language representation makes it harder to ground logical inferences in perception, since perceptual representations do not seem to include corresponding elements. The larger discrepancy between the logical and non-logical representations as above also poses a problem for the computational theory of human-level intelligence. Such a computational theory typically uses both logical and non-logical methods depending on the task, and each of these computational methods uses a different kind of representational structure. In order to improve the efficiency of the spontaneous data-exchange between the logical and non-logical computation methods, it is better to make the knowledge representation that