The Computational Theory of Intelligence: Information Entropy

This paper attempts to introduce a computational approach to the study of intelligence that the researcher has accumulated over years of study. This approach takes into account data from psychology, neurology, artificial intelligence, machine learning, and mathematics. Central to this framework is the fact that the goal of any intelligent agent is to reduce the randomness in its environment in some meaningful way. Of course, formal definitions in the context of this paper for terms like "intelligence", "environment", and "agent" will follow. The approach draws from multidisciplinary research and has many applications. We will utilize the construct in discussions at the end of the paper. Other applications will follow in future works. Implementations of this framework can apply to many fields of study including general artificial intelligence (GAI), machine learning, optimization, information gathering, clustering, and big data, and extend outside of the applied mathematics and computer science realm to even more areas including sociology, psychology, and neurology, and even philosophy.