Research on a Learning Model Presuming Representationalism, Functionalism, and Neural Darwinism

Even the best artificial intelligence technology can not compare with humans on the induc-tive ability. Humans can learn general rules from observed instances based only on bare-bone prior knowledge. Lots of research on the human mind tries to explain the learning ability of humans. Of all these hypotheses about the human mind, this thesis designs a learning model based on the ideas of representationalism, functionalism, and neural Darwinism. This thesis researches on the learning ability of the proposed model when the model is given only bare-bone prior knowledge. The proposed model is composed of three parts, which are respectively founded on representationalism, functionalism, and neural Darwinism. The knowledge repre-sentation of the proposed model is designed based on representationalism: symbols are the basic components of the knowledge representation of the model. The combination of knowl-edge of the proposed model is designed based on functionalism: the knowledge in the model can be combined in the form of functions. The inductive ability of the proposed model is de-signed based on neural Darwinism: the model exploits evolutionary processes to learn general rules. This thesis analyzes the proposed learning model. The expressive power of the mode