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检索条件"主题词=INDUCTIVE LOGIC PROGRAMMING"
524 条 记 录,以下是61-70 订阅
排序:
FOLD-RM: A Scalable, Efficient, and Explainable inductive Learning Algorithm for Multi-Category Classification of Mixed Data
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THEORY AND PRACTICE OF logic programming 2022年 第5期22卷 658-677页
作者: Wang, Huaduo Shakerin, Farhad Gupta, Gopal Univ Texas Dallas Richardson TX 75083 USA
FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classi... 详细信息
来源: 评论
Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems
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THEORY AND PRACTICE OF logic programming 2022年 第4期22卷 606-622页
作者: Tarzariol, Alice Schekotihin, Konstantin Gebser, Martin Law, Mark Univ Klagenfurt Klagenfurt Austria Graz Univ Technol Graz Austria Imperial Coll London London England
Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, ex... 详细信息
来源: 评论
Neural-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes  17
Neural-Symbolic Predicate Invention: Learning Relational Con...
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17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy)
作者: Sha, Jingyuan Shindo, Hikaru Kersting, Kristian Dhami, Devendra Singh Tech Univ Darmstadt Darmstadt Germany Hessian Ctr Artificial Intelligence Hessian AI Darmstadt Germany German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany
The predicates used for inductive logic programming (ILP) systems are usually elusive and need to be hand-crafted in advance, which limits the generalization of the system when learning new rules without sufficient ba... 详细信息
来源: 评论
Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment
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MACHINE LEARNING 2022年 第2期111卷 575-623页
作者: Dash, Tirtharaj Srinivasan, Ashwin Baskar, A. BITS Pilani APPCAIR KK Birla Goa Campus Pilani 403726 Goa India BITS Pilani Dept CS & IS KK Birla Goa Campus Pilani 403726 Goa India
We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on mode-directed inverse entailment (MDIE) developed in inductive... 详细信息
来源: 评论
Meta-interpretive learning as metarule specialisation
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MACHINE LEARNING 2022年 第10期111卷 3703-3731页
作者: Patsantzis, S. Muggleton, S. H. Imperial Coll London London England
In Meta-interpretive learning (MIL) the metarules, second-order datalog clauses acting as inductive bias, are manually defined by the user. In this work we show that second-order metarules for MIL can be learned by MI... 详细信息
来源: 评论
Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework
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MACHINE LEARNING 2022年 第4期111卷 1523-1549页
作者: Mitchener, Ludovico Tuckey, David Crosby, Matthew Russo, Alessandra Imperial Coll London Exhibit Rd London SW7 2BX England
In this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act compo... 详细信息
来源: 评论
Explaining Optimal Trajectories  7th
Explaining Optimal Trajectories
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7th International Joint Conference on Rules and Reasoning (RuleML+RR)
作者: Rouveirol, Celine Aoual, Malik Kazi Soldano, Henry Ventos, Veronique Nukkai Paris France Univ Sorbonne Paris Nord CNRS UMR 7030 Inst GalileeLIPN Villetaneuse France ISYEB Museum Natl Hist Nat CNRS UMR 7205 Paris France
We propose a definition of common explanation for the label shared by a group of observations described as first order interpretations, and provide algorithms to enumerate minimal common explanations. This was motivat... 详细信息
来源: 评论
Learning Distributional Programs for Relational Autocompletion
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THEORY AND PRACTICE OF logic programming 2022年 第1期22卷 81-114页
作者: Kumar, Nitesh Kuzelka, Ondrej De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Leuven Belgium Czech Tech Univ Dept Comp Sci Prague Czech Republic
Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses... 详细信息
来源: 评论
FOLD-SE: An Efficient Rule-Based Machine Learning Algorithm with Scalable Explainability  26th
FOLD-SE: An Efficient Rule-Based Machine Learning Algorithm ...
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26th International Symposium on Practical Aspects of Declarative Languages (PADL)
作者: Wang, Huaduo Gupta, Gopal Univ Texas Dallas Richardson TX 75080 USA
We present FOLD-SE, an efficient, explainable machine learning algorithm for classification tasks given tabular data containing numerical and categorical values. The (explainable) model generated by FOLD-SE is represe... 详细信息
来源: 评论
On the non-efficient PAC learnability of conjunctive queries
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INFORMATION PROCESSING LETTERS 2024年 183卷
作者: Cate, Balder ten Funk, Maurice Jung, Jean Christoph Lutz, Carsten Univ Amsterdam ILLC Postbus 94242 NL-1090 GE Amsterdam Netherlands Univ Leipzig Augustuspl 10 D-04109 Leipzig Germany TU Dortmund Univ August Schmidt Str 1 D-44227 Dortmund Germany
This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention ... 详细信息
来源: 评论