MindForth as a natural-language (NL) comprehension system
In the MindForth artificial general intelligence (AGI) system, natural- language (NL) generation and comprehension are a two-way street. Just as MindForth generates a thought in English by letting sp reading activation link concept to concept to concept in a linguistic superstructure sprawling over the conceptual mindgrid, likewise NL comprehension in MindForth consists in laying down linkages from concept to concept to concept, so that the idea being comprehended is recoverable or regenerable at some future time when spreading activation follows a meandering chain of thought to the comprehended idea, or proposition, or assertion. Being still a primitive AGI, MindForth can comprehend only primitive natural language. In comparison with other NL comprehension systems, MindForth most likely stands out as being based on its own project-specific theory of mind which relies not on any ontological knowledge- base (KB) but rather on a conceptual knowledge-base as the substrate for both generation and comprehension. Other systems may generate responses to KB queries without actually generating a conceptual thought, and may therefore be incapable of comprehension for lack of conceptual underpinnings. To assess the capability of an AGI system in NL comprehension, one should look for the change in state that occurs before and after the input of the NL input to be comprehended. In MindForth, the input is integrated not only with the knowledge-base as a raw assertion, but may also be integrated in a broader sense as MindForth expands its ability to think recursively and inferentially about the raw assertions which it incorporates into its knowledge base.