用范畴逻辑设计更强大符号推理智能体，支持复杂对象建模。
Logic-Based Artificial Intelligence Algorithms Supporting Categorical Semantics
- 基于范畴逻辑构建前向链式与正规化推理算法
- 支持多类型理论与一阶逻辑片段的统一处理
- 适用于不满足经典逻辑的语义范畴，适合形式推理研究者
本文将范畴逻辑应用于设计能够对结构远超集合的对象进行符号推理的人工智能代理。基于Johnstone的上下文项与公式中的序列演算，我们开发了在具有霍恩逻辑规则的笛卡尔范畴中进行推理的前向链式与正规化算法。同时，我们对一阶合一进行了改进，以支持多类型理论、上下文及一阶逻辑的片段。这些重构的意义在于，它们可应用于不支持经典逻辑甚至全部逻辑连接词的语义范畴中的推理。
原文摘要 · Abstract (English)
This paper seeks to apply categorical logic to the design of artificial intelligent agents that reason symbolically about objects more richly structured than sets. Using Johnstone's sequent calculus of terms- and formulae-in-context, we develop forward chaining and normal form algorithms for reasoning about objects in cartesian categories with the rules for Horn logic. We also adapt first-order unification to support multi-sorted theories, contexts, and fragments of first-order logic. The significance of these reformulations rests in the fact that they can be applied to reasoning about objects in semantic categories that do not support classical logic or even all its connectives.
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