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In AI research, so far, the attention paid to the characterization andrepresentation of function and affordance has been sporadic and sparse, eventhough this aspect features prominently in an intelligent system's functioning.In the sporadic and sparse, though commendable efforts so far devoted to thecharacterization and understanding of function and affordance, there has alsobeen no general framework that could unify all the different use domains andsituations related to the representation and application of functionalconcepts. This paper develops just such a general framework, with an approachthat emphasizes the fact that the representations involved must be explicitlycognitive and conceptual, and they must also contain causal characterizationsof the events and processes involved, as well as employ conceptual constructsthat are grounded in the referents to which they refer, in order to achievemaximal generality. The basic general framework is described, along with a setof basic guiding principles with regards to the representation offunctionality. To properly and adequately characterize and representfunctionality, a descriptive representation language is needed. This languageis defined and developed, and many examples of its use are described. Thegeneral framework is developed based on an extension of the general languagemeaning representational framework called conceptual dependency. To support thegeneral characterization and representation of functionality, the basicconceptual dependency framework is enhanced with representational devicescalled structure anchor and conceptual dependency elaboration, together withthe definition of a set of ground level concepts. These novel representationalconstructs are defined, developed, and described. A general framework dealingwith functionality would represent a major step toward achieving ArtificialGeneral Intelligence.

A General Framework for the Representation of Function and Affordance: A Cognitive, Causal, and Grounded Approach, and a Step Toward AGI