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检索条件"主题词=INDUCTIVE LOGIC PROGRAMMING"
524 条 记 录,以下是51-60 订阅
排序:
Explanatory machine learning for sequential human teaching
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MACHINE LEARNING 2023年 第10期112卷 3591-3632页
作者: Ai, Lun Langer, Johannes Muggleton, Stephen H. Schmid, Ute Imperial Coll London Dept Comp London England Univ Bamberg Bamberg Germany Univ Bamberg Cognit Syst Grp Bamberg Germany
The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. inductive logic programming uses logic programming to derive logic theories from small data based on abduction and in... 详细信息
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An ILASP-Based Approach to Repair Petri Nets  17th
An ILASP-Based Approach to Repair Petri Nets
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17th International Conference on logic programming and Non-monotonic Reasoning
作者: Chiariello, Francesco Ielo, Antonio Tarzariol, Alice Univ Toulouse IRIT ANITI Toulouse France Univ Calabria Arcavacata Di Rende Italy Univ Klagenfurt Klagenfurt Austria
Petri nets are a class of models of computation used to compactly represent discrete event systems. Among many application domains, they have now become the most prominent formalism to express process models in Proces... 详细信息
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From SMT to ASP: Solver-Based Approaches to Solving Datalog Synthesis-as-Rule-Selection Problems
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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2023年 第POPL期7卷 185-217页
作者: Bembenek, Aaron Greenberg, Michael Chong, Stephen Harvard Univ Cambridge MA 02138 USA Stevens Inst Technol Hoboken NJ 07030 USA
Given a set of candidate Datalog rules, the Datalog synthesis-as-rule-selection problem chooses a subset of these rules that satisfies a specification (such as an input-output example). Building off prior work using c... 详细信息
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Learning answer set programs with aggregates via sampling and genetic programming
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MACHINE LEARNING 2025年 第7期114卷 1-23页
作者: Azzolini, Damiano Univ Ferrara Dept Environm & Prevent Sci Ferrara Italy
The goal of inductive logic programming is to learn a logic program that models the examples provided as input. The search space of the possible programs is constrained by a language bias, which defines the atoms and ... 详细信息
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Algebraic Connection Between logic programming and Machine Learning (Extended Abstract)  17th
Algebraic Connection Between Logic Programming and Machine L...
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17th International Symposium on Functional and logic programming (FLOPS)
作者: Inoue, Katsumi Natl Inst Informat 2-1-2 HitotsubashiChiyoda Ku Tokyo 1018430 Japan
There have been attempts to connect machine learning and symbolic reasoning, providing interfaces between them. This work focuses on our original approach to integrate machine learning and symbolic reasoning, in the c... 详细信息
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Explaining with Attribute-Based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust  1
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31st International Conference on inductive logic programming (ILP)
作者: Finzel, Bettina Kuhn, Simon P. Tafler, David E. Schmid, Ute Univ Bamberg Cognit Syst Weberei 5 D-96047 Bamberg Germany
Explaining concepts by contrasting examples is an efficient and convenient way of giving insights into the reasons behind a classification decision. This is of particular interest in decision-critical domains, such as... 详细信息
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Leveraging Neurosymbolic AI for Slice Discovery  18th
Leveraging Neurosymbolic AI for Slice Discovery
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18th International Conference on Neural-Symbolic Learning and Reasoning (NeSy)
作者: Collevati, Michele Eiter, Thomas Higuera, Nelson Tech Univ Wien Inst Log & Computat Favoritenstr 9-11 A-1040 Vienna Austria
While remarkable recent developments in deep neural networks have significantly contributed to advancing the state-of-the-art in Computer Vision (CV), several studies have also shown their limitations and defects. In ... 详细信息
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Hypergraph Neural Networks with logic Clauses
Hypergraph Neural Networks with Logic Clauses
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International Joint Conference on Neural Networks (IJCNN)
作者: Gandarela de Souza, Joao Pedro Zaverucha, Gerson Garcez, Artur S. d'Avila Univ Fed Rio De Janeiro COPPE Rio De Janeiro Brazil City Univ London Dept Comp Sci London England
The analysis of structure in complex datasets has become essential to solving difficult Machine Learning problems. Relational aspects of data, capturing relationships between objects, play a crucial role in understand... 详细信息
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Learning any memory-less discrete semantics for dynamical systems represented by logic programs
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MACHINE LEARNING 2022年 第10期111卷 3593-3670页
作者: Ribeiro, Tony Folschette, Maxime Magnin, Morgan Inoue, Katsumi Univ Nantes LS2N CNRS Cent Nantes F-44000 Nantes France Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan Univ Lille Cent Lille CNRS UMR CRIStAL 9189 F-59000 Lille France
Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far the systems that LFIT handled were mainly restricted to... 详细信息
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Learning explanations for biological feedback with delays using an event calculus
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MACHINE LEARNING 2022年 第7期111卷 2435-2487页
作者: Srinivasan, Ashwin Bain, Michael Baskar, A. BITS Pilani Dept CSIS & APPCAIR Goa Campus Sancoale Goa India Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia BITS Pilani Dept Comp Sci & Informat Syst Sancoale Goa India
We propose the identification of feedback mechanisms in biological systems by learning logical rules in R. Thomas' Kinetic logic (Thomas and D'Ari in Biological feedback. CRC Press, 1990). The principal advant... 详细信息
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