Organizing cognitive schemas by information to improve problem solving reuse
Cognitive Schemas, Laws and Tasks
Artificial Intelligence
Summary
Figuring out how our mental shortcuts or 'schemas' can be clearly represented to help solve problems better is a tricky challenge. The authors created a framework that organizes these schemas based on the information and relationships they need instead of just treating them as isolated concepts. This approach helps separate what limits our understanding from what limits how we solve problems and shows when simplifying representations won't lose important details. Their work provides a way to reuse knowledge across different tasks, which could help build smarter systems that combine known facts and learned strategies.
What this means in practice
- •For knowledge engineers: Design knowledge bases that separate stable facts from task-specific contexts to improve problem-solving reusability.
- •For artificial intelligence developers: Build AI systems that integrate structured knowledge with flexible candidate generation while keeping representation consistent across tasks.
A theory result. No direct application yet.
Authors
Antal Jakovác, András Telcs
Abstract
This paper asks how explicit representations can support reusable cognitive schemas in knowledge-based problem solving. We develop a structural framework in which schemas are organized by the information and relations required for their use, rather than introduced as unrelated primitives. The framework also distinguishes context-dependent relations from more stable structures that can be reused across different representations. Tasks are described through the information available, the unknowns to be determined, and the constraints that admissible solutions must satisfy. This makes it possible to separate limitations of the representation from limitations of the solving procedure. In particular, we distinguish inconsistency, underdetermination, and contextual insufficiency, where the current representation lacks distinctions or relations required by the external task meaning. We also show formally when a reduction of representation preserves the task-relevant solution structure. The resulting task--schema interface offers a structured way to describe representational conditions relevant to problem solving. It supports the reuse and stabilization of derived knowledge while remaining independent of the particular mechanism used to generate candidate solutions. This may provide a useful component for future solver architectures that combine structured knowledge, verification, and learned proposal mechanisms.