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A History of Probabilistic Inductive Logic Programming [PDF]
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 +5 more sources
Inductive logic programming at 30 [PDF]
Machine 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 +5 more sources
The Inductive Constraint Programming Loop [PDF]
IEEE Intelligent Systems, 2015 Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling, and resource allocation problems, all while we continuously gather vast amounts of data about these problems.
C. Bessiere +9 more
semanticscholar +11 more sources
Inductive programming meets the real world [PDF]
Communications of the ACM, 2015 Inductive programming can liberate users from performing tedious and repetitive tasks.
Sumit Gulwani +5 more
semanticscholar +6 more sources
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
semanticscholar +3 more sources
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
semanticscholar +3 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
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
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
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