我今天要讲的是ILP,但是我会把整个 周志华 机器学习 15章 规则学习都讲一遍,因为这部分是一个系统的知识,环环相扣。 参考资料:周志华 机器学习 2016年1月第1版 第15章 规则学习 15.1 基本概念 这部分我觉得还好理解,我主要说一下什么是一阶规则,我看的时候不能理解一阶的含义,我觉得百科里面的解释就很好: ...
Therefore a brief overview of extra-logical constraints used in ILP systems is given. Some present limitations and research directions for the field are identified.doi:10.1145/181668.181671MUGGLETON,S.ACMAcm Sigart BulletinS. Muggleton. Inductive logic programming: derivations, successes and shortcomings....
Inductive Logic Programming (ILP) is a rule-based learning method that seeks underlying patterns in data by deriving a set of if-then logic rules. These rules describe positive instances but not negative instances, and they are usually constrained to be Horn clauses. A Horn clause consists of...
归纳逻辑编程(Inductive logic programming, ILP)目的是从有标记的数据中学习逻辑规则。由于规则是明确符号化的,因此它们比黑盒模型具有一定的优势。所学到的规则可以被检查、理解和验证,形成一种存储所学知识的方便手段。 例如合取和析取的逻辑符号是不可微分的,大多数神经符号ILP技术必须解决的一个问题是如何使用基于...
ILP 200919th International Conference on Inductive Logic Programming Jul 2, 2009 - Jul 4, 2009Leuven, BelgiumApr 3, 2009 Present CFP : 2018 Submissions must describe relevant and novel results on the following typical, but not exclusive, topics: ...
Inductive logic programming (ILP) [1] refers to a broad class of problems that aim to find logic rules that model the observed data. The observed data usually contains background knowledge and examples, typically in the form of database relations or knowledge graphs. Inductive logic programming ...
Areas of interest include, but are not limited to: Theory of ILP, foundations of logical & relational learning, computational learning theory. Learning in various logical representations and formalisms, such as logic programming & answer set programming, first-order & higher-order logic, description...
We propose a novel paradigm for solving Inductive Logic Programming (ILP) problems via deep recurrent neural networks. This proposed ILP solver is designed based on differentiable implementation of the deduction via forward chaining. In contrast to the majority of past methods, instead of searching th...
In this paper, we propose a novel deep RRL based on a differentiable Inductive Logic Programming (ILP) that can effectively learn relational information from image and present the state of the environment as first order logic predicates. Additionally, it can take the expert background knowledge ...