Results 31 to 40 of about 201,128 (321)
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
المنطق الماصدقي: تاريخه وخصائصه وتطبيقاته [PDF]
Maǧallaẗ Kulliyyaẗ Al-ādāb Ǧāmiʿaẗ Būrsaʿīd, 2022 لم يُعرف التمييز بين حدي القضية - المفهوم والماصدق - بشکلٍ انفصالي کلٌ على حدة إلاَّ في وقتٍ متأخر؛ فکل قضية تتکون من حدين هما المفهوم والماصدق، والعلاقة بينهما عکسية کما نعلم؛ کلما زاد المفهوم قل الماصدق والعکس، لکن هذا لا يعني القول بأحدهما فقط دون ...
محمد سيد محمد أبوالعلا
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Extending Coinductive Logic Programming with Co-Facts [PDF]
Electronic Proceedings in Theoretical Computer Science, 2017 We introduce a generalized logic programming paradigm where programs, consisting of facts and rules with the usual syntax, can be enriched by co-facts, which syntactically resemble facts but have a special meaning.
Davide Ancona +2 more
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Program Logics for Homogeneous Generative Run-Time Meta-Programming [PDF]
Logical Methods in Computer Science, 2015 This paper provides the first program logic for homogeneous generative run-time meta-programming---using a variant of MiniML by Davies and Pfenning as its underlying meta-programming language.
Martin Berger, Laurence Tratt
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Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models [PDF]
Electronic Proceedings in Theoretical Computer Science, 2019 We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of the High-Utility Itemset Mining (HUIM) problem.
Farhad Shakerin
doaj +1 more source
NUML International Journal of Engineering and Computing, 2023 Industries widely use three-phase inductive loads, such as induction motors, due to their cost-effectiveness, low maintenance, reliability, and durability.
Amir Hamza +4 more
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FastLAS: Scalable Inductive Logic Programming Incorporating Domain-Specific Optimisation Criteria
AAAI Conference on Artificial Intelligence, 2020 Inductive Logic Programming (ILP) systems aim to find a set of logical rules, called a hypothesis, that explain a set of examples. In cases where many such hypotheses exist, ILP systems often bias towards shorter solutions, leading to highly general ...
Mark Law +4 more
semanticscholar +1 more source
Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Explaining Biases in Machine Learning
Computers, 2021 Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning.
Alfonso Ortega +6 more
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Active Inductive Logic Programming for Code Search [PDF]
International Conference on Software Engineering, 2018 Modern search techniques either cannot efficiently incorporate human feedback to refine search results or cannot express structural or semantic properties of desired code.
Aishwarya Sivaraman +3 more
semanticscholar +1 more source
Few-Shot Induction of Generalized Logical Concepts via Human Guidance
Frontiers in Robotics and AI, 2020 We consider the problem of learning generalized first-order representations of concepts from a small number of examples. We augment an inductive logic programming learner with 2 novel contributions.
Mayukh Das +3 more
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