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Conflict-Driven Inductive Logic Programming [PDF]
Theory and Practice of Logic Programming, 2022 The goal of inductive logic programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns answer set programs (ASP).
Mark Law
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The Inductive Constraint Programming Loop [PDF]
IEEE Intelligent Systems, 2016 Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans.
Bessiere, Christian +9 more
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Mathematical applications of inductive logic programming [PDF]
, 2006 Accepted ...
G. Sutcliffe +7 more
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Incremental Learning of Event Definitions with Inductive Logic Programming [PDF]
Machine-mediated learning, 2014 Event recognition systems rely on knowledge bases of event definitions to infer occurrences of events in time. Using a logical framework for representing and reasoning about events offers direct connections to machine learning, via Inductive Logic ...
Nikos Katzouris +2 more
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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
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 +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
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