A Structural Theory of Cognitive Representation and Problem Solving,Contexts, Invariance, and the Knowledge Space
We propose a minimal structural framework in which representational operations relevant to problem solving, such as context formation, invariance recognition, representative selection, abstraction, and procedural reuse, are made explicit.
ProofPaper ↗
Key points
- Learning and problem solving depend critically on the structure of internal representations.
- While many modern data-driven artificial systems achieve strong predictive performance, their learned representations often lack explicit structure for expressing abstraction, invariance, and task-relevant regularities.
- Using simple illustrative examples and a finite weak-solver demonstration, we show that appropriate representational organization can simplify the form and scope of admissible regularities, even when problem solving is carried out by a fixed and limited solver.
- The contribution of the paper is structural rather than algorithmic: it identifies representational prerequisites for abstraction, invariance, and procedural reuse in problem solving, and states explicit success and failure conditions for the weak-solver setting.
Sources (1)
- [1]A Structural Theory of Cognitive Representation and Problem Solving,Contexts, Invariance, and the Knowledge SpacearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 04:54 PM
We propose a minimal structural framework in which representational operations relevant to problem solving, such as context formation, invariance recognition, representative selection, abstraction, and procedural reuse, are made explicit.
Learning and problem solving depend critically on the structure of internal representations.
Extractive summary: sentences quoted from the sources.
