发表机构
HUN-REN Wigner Research Centre for Physics（HUN-REN 维格纳物理研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个结构框架，通过信息与关系组织可复用认知图式，区分表示与求解限制，并形式化表示约简的条件，为问题求解提供任务-图式接口。
AI 中文摘要
本文探讨显式表示如何在基于知识的问题求解中支持可复用的认知图式。我们开发了一个结构框架，在该框架中，图式根据其使用所需的信息和关系进行组织，而不是作为无关的原始元素引入。该框架还区分了依赖于上下文的关系与可在不同表示中复用的更稳定结构。任务通过可用信息、待确定的未知量以及可接受解必须满足的约束来描述。这使得区分表示的限制与求解过程的限制成为可能。特别地，我们区分了不一致性、欠定性和上下文不足性，其中当前表示缺乏外部任务含义所需的区分或关系。我们还形式化地证明了表示的约简在何种情况下保留与任务相关的解结构。由此产生的任务-图式接口提供了一种结构化的方式来描述与问题求解相关的表示条件。它支持派生知识的复用和稳定化，同时保持独立于用于生成候选解的具体机制。这可能为未来结合结构化知识、验证和学习型提议机制的求解器架构提供有用的组件。
英文摘要
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.
Comments25 pages , 1 table
