【代数语言学巡礼】归纳逻辑编程(inductive logic programming)III 归纳逻辑是事例证据支持的逻辑。在演绎逻辑中,一个有效的演绎论证的前提在逻辑上必然包含结论,逻辑蕴涵意味着使前提为真的每一个逻辑上可能的状态也必须使结论成为真理。因此,一个有效的演绎论点的前提为结论提供了完全的支持。然而归纳逻辑将这种观点扩展...
In addition LLL is starting to develop its own character as a sub-discipline of AI involving the confluence of computational linguistics, machine learning and logic programming.doi:10.1016/S0004-3702(99)00067-3Stephen MuggletonArtificial Intelligence...
Haipeng Ding, in AI Open, 2021 2.2 Inductive logic programming (ILP) Inductive Logic Programming (ILP) aims at seeking underlying patterns formulated by logic programs/rules/formulas shared in the data. It is one of the rule-based learning methods which derive a set of if-then logic rules ...
This chapter provides a short overview of a GA-based system for inductive concept learning (in a fragment of first-order logic) . The described system exploits problem—specific knowledge by means of ad-hoc selection, mutation operators, and optimization
"A History of Probabilistic Inductive Logic Programming". In: Frontiers in Robotics and AI 1 (2014), p. 6.Fabrizio Riguzzi, Elena Bellodi, and Riccardo Zese. A history of probabilistic inductive logic programming. Frontiers in Robotics and AI, 1:1-5, 2014....
Probabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic
Popper dispenses with inductive logic and relies instead on testing. 波普尔摒弃了归纳逻辑,代之以验证。 article.yeeyan.org 5. This method obviously is a method of inductive logic. 这种方法显然是一种归纳逻辑方法。 word.hcbus.com 6. Survey of Studies of Inductive Logic and AI in U. S. A....
An inductive definition in Computer Science refers to the process of defining new operators using fixed-points of inductive definitions, allowing for the iteration of hyperjumps and the creation of constructive number classes. AI generated definition based on: Studies in Logic and the Foundations of ...
combinatorial generalization 在许多结构化方法中至关重要,包括logic, grammars, classic planning, graphical models, causal reasoning, Bayesian nonparametrics, and probabilistic programming。整个子领域都专注于以实体和关系为中心的显式学习,例如关系强化学习 (稍后展开) 和统计关系学习。 现代深度学习方法经常遵循端到...
Interaction of inductive and deductive techniques for AI solutions, Integration of Answer Set Programming (ASP) in inductive scenarios, Integration of Constraint Programming (CSP) in inductive scenarios, Integration of other logic programming paradigms in inductive scenarios, ...