Toward Human-Level Artificial Intelligence - Representation and Computation of Meaning in Natural Language

This doctoral thesis presents a novel research approach toward human-level artificial intelligence. The approach involves developing an AI system using a language of thought based on the unconstrained syntax of a natural language; designing this system as a collection of concepts that can create and modify concepts, expressed in the language of thought, to behave intelligently in an environment; and using methods from cognitive linguistics such as mental spaces and conceptual blends for multiple levels of mental representation and computation. Proposing a design inspection alternative to the Turing Test, these pages discuss ‘higherlevel mentalities’ of human intelligence, which include natural language understanding, higher-level forms of learning and reasoning, imagination, and consciousness. This thesis endeavors to address all the major theoretical issues and objections that might be raised against its approach, or against the possibility of achieving human-level AI in principle. No insurmountable objections are identified, and arguments refuting several objections are presented. This thesis describes the design of a prototype demonstration system, and discusses processing within the system that illustrates the potential of the research approach to achieve human-level AI. This thesis cannot claim to actually achieve human-level AI, it can only present an approach that may eventually reach this goal.