mlsanityData to 14 Oct 2026

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

arXiv 2206.05273 ↗First posted 2 Jun 2022cs.AIcited by 3

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Abstract (arXiv)

In AI research, so far, the attention paid to the characterization and representation of function and affordance has been sporadic and sparse, even though this aspect features prominently in an intelligent system's functioning. In the sporadic and sparse, though commendable efforts so far devoted to the characterization and understanding of function and affordance, there has also been no general framework that could unify all the different use domains and situations related to the representation and application of functional concepts. This paper develops just such a general framework, with an approach that emphasizes the fact that the representations involved must be explicitly cognitive and conceptual, and they must also contain causal characterizations of the events and processes involved, as well as employ conceptual constructs that are grounded in the referents to which they refer, in order to achieve maximal generality. The basic general framework is described, along with a set of basic guiding principles with regards to the representation of functionality. To properly and adequately characterize and represent functionality, a descriptive representation language is needed. This language is defined and developed, and many examples of its use are described. The general framework is developed based on an extension of the general language meaning representational framework called conceptual dependency. To support the general characterization and representation of functionality, the basic conceptual dependency framework is enhanced with representational devices called structure anchor and conceptual dependency elaboration, together with the definition of a set of ground level concepts. These novel representational constructs are defined, developed, and described. A general framework dealing with functionality would represent a major step toward achieving Artificial General Intelligence.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 6 of its 112 references that are in the map. First posted on arXiv 2 Jun 2022.

Builds on

6 of 112 references.

References · 6

  1. 2020Integrated Task and Motion Planningcited by 290
  2. 2018Holistic 3D Scene Parsing and Reconstruction from a Single RGB Imagecited by 69
  3. 2020Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sensecited by 62
  4. 2021Why AI is Harder Than We Thinkcited by 41
  5. 2019Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learningcited by 11
  6. 2019Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Bodycited by 6

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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.