Automatic discovery of quality dimensions for abstract concepts in conceptual spaces

Develop data-driven methods to automatically discover interpretable quality dimensions for abstract concepts within the conceptual spaces framework, enabling dimension grounding without relying solely on heuristic expert specification.

Background

The paper’s chess proof-of-concept defines seven quality dimensions and corresponding domains using expert heuristics. While this enables initial region anchoring for strategies such as King Attack, Positional Sacrifice, and Space Domination, the authors emphasize that relying on expert-defined dimensions limits scalability and introduces subjectivity.

They explicitly identify the automatic discovery of quality dimensions for abstract, temporally evolving concepts as a significant open research question, highlighting the need for data-driven mechanisms to ground interpretable dimensions in conceptual spaces beyond perceptual domains.

References

The problem of automatically discovering quality dimensions for abstract concepts, learning concept boundaries from observational data, and validating geometric representations against human expert knowledge represents significant open research questions for both artificial intelligence and conceptual spaces theory.

— Abstract Concept Modelling in Conceptual Spaces: A Study on Chess Strategies  (2601.21771 - Banaee et al., 29 Jan 2026) in Discussion — Subsection “Challenges and Implications”