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)00067-3
Journal 2021, AI OpenJing Zhang, ... Haipeng Ding Chapter Knowledge Representation 4.2.2 Inductive logic programming In inductive logic programming (ILP) the task of the learning algorithm is to induce, given the background knowledge B, a set of positive learning examples E+ and a set of neg...
"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....
In 2009, three international conferences / workshops on learning from relational, graph-based and probabilistic knowledge will be co-located: ILP-2009, the 19th International Conference on Inductive Logic Programming; MLG-2009, the 7th International Workshop on Mining and Learning with Graphs; and SR...
In many real-world applications, acquiring large amounts of training data can be difficult or impossible. This paper presents an efficient and explainable method for few-shot learning from images using inductive logic programming (ILP). ILP utilises logical representations and reasoning to capture ...
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....
Current research within the Bristol AI Group aims to combine the best aspects of inductive logic programming with the uncertainty representation of Fril to create a sophisticated and novel approach to knowledge discovery. In order to do this, ...
logic, grammars, classic planning, graphical models, causal reasoning, Bayesian nonparametrics, and probabilistic programming 其下面的子领域都明确的以实体和关系为中心进行学习 为什么结构化的方法如此重要? 这也是本文强调的一个重点, 结构化对应的是这篇论文的主题, 也就是关系归纳偏置. 其对应的是神经网络模型...
In the field of probabilistic logic programming usually the aim is to learn these kinds of models to predict specific atoms or predicates of the domain, called target atoms/predicates. However, it might also be useful to learn classifiers for interpretations as a whole: to this end, we ...
to calculate the complexity of the set of all indices of monotone Σ10-definitions which are computable. We also examine the complexity of new type of inductive definition which we callweakly finitarymonotone inductive definitions. Applications are given in proof theory and in logic programming....