Toward human-like intelligence for complex-problems solving. Proposing a Modular Framework for Machine-Learning to reach partial Human-like Intelligence based on Neocortex and Human Psychology Theories.

We live in an exciting time when speculations and theories in cognitive science, neuroscience, and artificial intelligence are influencing each other toward understanding the mechanisms of the human brain. In this study, we propose to develop a framework for complex problem-solving by enhancing the field of articial intelligence with human-like intelligence, which merges the fields of psychology and neuroscience with computer science. The intended modular framework would use distributed consensuses of non-deterministic world models, mimick- ing psychological theories of human exhibited intelligence, and theorized brain mechanisms such as continuous learning over unforeseen data.