在模糊时态约束 Prolog 中为推导添加确定度:FTCProlog

Axioms Pub Date : 2024-07-12 DOI:10.3390/axioms13070472
María-Antonia Cárdenas-Viedma
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引用次数: 0

摘要

在大多数人工智能相关应用中,时间管理都是必不可少的。此外,我们知道时间信息往往并不精确。事实上,在大多数情况下,有必要处理不精确和/或不确定性。另一方面,我们还需要处理许多时间语句中隐含的常识信息。在本文中,我们介绍了 FTCProlog,这是一种能够合理有效地处理模糊时态约束的逻辑编程语言。FTCProlog 与其前身 PROLogic 的主要区别在于,它能将确定性指数与通过 SLD 解析获得的推导联系起来。这种解决方法基于理论逻辑框架 FTCLogic 中的一项建议。该模型集成了基于可能性逻辑的一阶逻辑和模糊时间约束网络(FTCN),可实现高效的时间管理。在一些应用中,人们希望验证某些事件之间的时间间隔在多大程度上遵循给定的时间模式,而确定性指数的计算在这些应用中非常有用。在本文中,我们证明了该指数的计算遵守了理论模型的语义属性。FTCProlog 是用 Haskell 实现的。
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Adding a Degree of Certainty to Deductions in a Fuzzy Temporal Constraint Prolog: FTCProlog
The management of time is essential in most AI-related applications. In addition, we know that temporal information is often not precise. In fact, in most cases, it is necessary to deal with imprecision and/or uncertainty. On the other hand, there is the need to handle the implicit common-sense information present in many temporal statements. In this paper, we present FTCProlog, a logic programming language capable of handling fuzzy temporal constraints soundly and efficiently. The main difference of FTCProlog with respect to its predecessor, PROLogic, is its ability to associate a certainty index with deductions obtained through SLD-resolution. This resolution is based on a proposal within the theoretical logical framework FTCLogic. This model integrates a first-order logic based on possibilistic logic with the Fuzzy Temporal Constraint Networks (FTCNs) that allow efficient time management. The calculation of the certainty index can be useful in applications where one wants to verify the extent to which the times elapsed between certain events follow a given temporal pattern. In this paper, we demonstrate that the calculation of this index respects the properties of the theoretical model regarding its semantics. FTCProlog is implemented in Haskell.
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