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Inductive Logic Programming [electronic resource] : 21st International Conference, ILP 2011, Windsor Great Park, UK, July 31 – August 3, 2011, Revised Selected Papers / edited by Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, Francesca A. Lisi.

By: Muggleton, Stephen H [editor.].
Contributor(s): Tamaddoni-Nezhad, Alireza [editor.] | Lisi, Francesca A [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 7207Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Description: XI, 406 p. 130 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642319518.Subject(s): Computer science | Logic design | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Mathematical Logic and Formal Languages | Programming Techniques | Logics and Meanings of Programs | Computation by Abstract DevicesDDC classification: 006.3 Online resources: Click here to access online
Contents:
Inference and Learning -- Beyond Reward: The Problem of Knowledge and Data -- Exploiting Constraints -- Online Bayesian Inference for the Parameters of PRISM Programs -- Learning Compact Markov Logic Networks with Decision Trees -- Relational Networks of Conditional Preferences -- k-Optimal: A Novel Approximate Inference Algorithm for ProbLog -- Learning Directed Relational Models with Recursive Dependencies -- Integrating Model Checking and Inductive Logic Programming -- Inductive Logic Programming in Answer Set Programming -- Graph-Based Relational Learning with a Polynomial Time Projection Algorithm -- Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models -- Conceptual Clustering of Multi-Relational Data -- Expressive Power of Safe First-Order Logical Decision Trees -- Relational Learning for Spatial Relation Extraction from Natural.
In: Springer eBooksSummary: This book constitutes the thoroughly refereed post-proceedings of the 21st International Conference on Inductive Logic Programming, ILP 2011, held in Windsor Great Park, UK, in July/August 2011. The 24 revised full papers were carefully reviewed and selected from 66 submissions. Also included are five extended abstracts and three invited talks. The papers represent the diversity and vitality in present ILP research including ILP theory, implementations, probabilistic ILP, biological applications, sub-group discovery, grammatical inference, relational kernels, learning of Petri nets, spatial learning, graph-based learning, and learning of action models.
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Inference and Learning -- Beyond Reward: The Problem of Knowledge and Data -- Exploiting Constraints -- Online Bayesian Inference for the Parameters of PRISM Programs -- Learning Compact Markov Logic Networks with Decision Trees -- Relational Networks of Conditional Preferences -- k-Optimal: A Novel Approximate Inference Algorithm for ProbLog -- Learning Directed Relational Models with Recursive Dependencies -- Integrating Model Checking and Inductive Logic Programming -- Inductive Logic Programming in Answer Set Programming -- Graph-Based Relational Learning with a Polynomial Time Projection Algorithm -- Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models -- Conceptual Clustering of Multi-Relational Data -- Expressive Power of Safe First-Order Logical Decision Trees -- Relational Learning for Spatial Relation Extraction from Natural.

This book constitutes the thoroughly refereed post-proceedings of the 21st International Conference on Inductive Logic Programming, ILP 2011, held in Windsor Great Park, UK, in July/August 2011. The 24 revised full papers were carefully reviewed and selected from 66 submissions. Also included are five extended abstracts and three invited talks. The papers represent the diversity and vitality in present ILP research including ILP theory, implementations, probabilistic ILP, biological applications, sub-group discovery, grammatical inference, relational kernels, learning of Petri nets, spatial learning, graph-based learning, and learning of action models.

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