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Differentiable Inductive Logic Programming for Structured Examples [PDF]
AAAI Conference on Artificial Intelligence, 2021 The differentiable implementation of logic yields a seamless combination of symbolic reasoning and deep neural networks. Recent research, which has developed a differentiable framework to learn logic programs from examples, can even acquire reasonable ...
Hikaru Shindo +2 more
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A History of Probabilistic Inductive Logic Programming
Frontiers in Robotics and AI, 2014 The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming.
Fabrizio eRiguzzi +2 more
doaj +2 more sources
Initial Algebra Semantics for Cyclic Sharing Tree Structures [PDF]
Logical Methods in Computer Science, 2010 Terms are a concise representation of tree structures. Since they can be naturally defined by an inductive type, they offer data structures in functional programming and mechanised reasoning with useful principles such as structural induction and ...
Makoto Hamana
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Cubical agda: a dependently typed programming language with univalence and higher inductive types [PDF]
Journal of functional programming, 2019 Proof assistants based on dependent type theory provide expressive languages for both programming and proving within the same system. However, all of the major implementations lack powerful extensionality principles for reasoning about equality, such as ...
Andrea Vezzosi +2 more
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Redesign and validation of a computer programming course using Inductive Teaching Method.
PLoS ONE, 2020 Inductive Teaching Method (ITM) promotes effective learning in technological education (Felder & Silverman, 1988). Students prefer ITM more as it makes the subject easily understandable (Goltermann, 2011).
Iftikhar Ahmed Khan +6 more
doaj +3 more sources
Family weight management in rural U.S. communities: a mixed methods study of parent and child perspectives [PDF]
BMC Public HealthBackground Effective treatments are available to address the rising prevalence of childhood obesity in the U.S. Families in rural communities face unique barriers to accessing and engaging in these programs.
Alyssa Button +8 more
doaj +2 more sources
Shrinking the Inductive Programming Search Space with Instruction Subsets [PDF]
International Conference on Agents and Artificial Intelligence, 2023 Inductive programming frequently relies on some form of search in order to identify candidate solutions. However, the size of the search space limits the use of inductive programming to the production of relatively small programs.
Edward McDaid, S. McDaid
semanticscholar +1 more source
Inductive logic programming at 30 [PDF]
Machine-mediated learning, 2021 Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge.
Andrew Cropper +3 more
semanticscholar +1 more source
Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2021 Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data.
Prithviraj Sen +3 more
semanticscholar +1 more source
Inductive logic programming at 30: a new introduction [PDF]
Journal of Artificial Intelligence Research, 2020 Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field.
Andrew Cropper, Sebastijan Dumancic
semanticscholar +1 more source