Selecting Between Semantic Modeling of Intelligence and Semantic Modeling of Systems

Abstract

The newly emerging science of Human-Centric Functional Modeling or HCFM models systems in terms of “functional state spaces” that are hypothesized to have the capacity to provide a complete representation of the behavior of any system modeled this way. When used for modeling human cognition, AI, or any other intelligent system, these functional state spaces potentially provide a complete representation of the meaning of any concepts as the functional states of cognition, as well as a complete representation of the meaning of reasoning processes used by such systems to navigate between concepts, thereby providing what is believed to be the first complete semantic model of information. When used for modeling other systems, these functional state spaces potentially provide a complete representation of the functional states those systems might occupy as well as the processes through which the systems might transition between those functional states. In addition, HCFM provides a functional definition for general problem-solving ability as well as the magnitude of that ability, and introduces the possibility of artificial systems which might exponentially increase that ability when information about the system is defined in terms of functional state spaces. This introduces two possibilities, the first is to represent information in terms of the functional state space of cognition and to increase capacity to solve problems regarding a second system where that information might or might not represent valid behaviors of that second system, or to represent information directly in terms of the functional state space of the second system, so all information represents valid behaviors of that system. This paper explores the implications of both.