Results 21 to 30 of about 201,128 (321)
Inductive Logic Programming: Theory and Methods
The Journal of Logic Programming, 1994 The paper is an interesting and clear survey of the theory and the applications of inductive logic programming (ILP). This is a new discipline arising from the integration of inductive machine learning and logic programming. Similarly to the case of inductive learning, the aim of ILP is to develop techniques for constructing inductively hypotheses from
S. Muggleton, L. D. Raedt
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Deep Inductive Logic Programming meets Reinforcement Learning [PDF]
International Conference on Logic Programming, 2023 One approach to explaining the hierarchical levels of understanding within a machine learning model is the symbolic method of inductive logic programming (ILP), which is data efficient and capable of learning first-order logic rules that can entail data ...
Andreas Bueff, Vaishak Belle
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Towards integrating fuzzy logic capabilities into an ontology-based Inductive Logic Programming framework [PDF]
International Conference on Intelligent Systems Design and Applications, 2011 Josué Iglesias, Jens Lehmann
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Inductive Logic Programming [PDF]
Lecture Notes in Computer Science, 2011 Inductive logic programming is the subfield of machine learning that uses first order logic to represent hypotheses and data. Because first order logic is expressive and declarative, inductive logic programming specfically targets problems involving structured data and background knowledge. Inductive logic programming tackles a wide variety of problems
水生 久木田
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Inductive Logic Programming in Clementine [PDF]
, 2000 This paper describes the integration of ILP with Clementine. Background on ILP and Clementine is provided, with a description of Clementine's target users. The benefits of ILP to data mining are outlined, and ILP is compared with pre-existing data mining algorithms. Issues of integration between ILP and Clementine are discussed.
Sam Brewer, Tom Khabaza
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Generating contrastive explanations for inductive logic programming based on a near miss approach [PDF]
Machine-mediated learning, 2021 In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cognitive science as well as in early
Johannes Rabold, M. Siebers, Ute Schmid
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A Critical Review of Inductive Logic Programming Techniques for Explainable AI [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption.
Zheng Zhang, L. Yilmaz, Bo Liu
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Rule Learning over Knowledge Graphs: A Review [PDF]
Transactions on Graph Data and Knowledge, 2023 Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks.
Wu, Hong +4 more
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Turning 30: New Ideas in Inductive Logic Programming [PDF]
International Joint Conference on Artificial Intelligence, 2020 Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data.
Andrew Cropper +2 more
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