Inductive Logic Programming
20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010, Revised Papers
Published on 13. June 2011
XI, 278 pages
978-3-642-21295-6 (ISBN)
Description
This book constitutes the thoroughly refereed post-proceedings of the 20th International Conference on Inductive Logic Programming, ILP 2010, held in Florence, Italy in June 2010.
The 11 revised full papers and 15 revised short papers presented together with abstracts of three invited talks were carefully reviewed and selected during two rounds of refereeing and revision. All current issues in inductive logic programming, i.e. in logic programming for machine learning are addressed, in particular statistical learning and other probabilistic approaches to machine learning are reflected.
More details
Other editions
Additional editions
Paolo Frasconi | Francesca A. Lisi
Inductive Logic Programming
20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010, Revised Papers
Book
05/2011
1st Edition
Springer
€53.49
Shipment within 5-7 days
Content
- 6489
- Preface
- Organization
- Table of Contents
- Abstracts of Invited Talks
- Rule Interchange Format : Logic Programming's Second Wind?
- Practical Probabilistic Programming
- Introduction
- References
- Probabilistic Relational Learning and Inductive Logic Programming at a Global Scale
- References
- Research Papers
- Learning Multi-class Theories in ILP
- Introduction
- Motivation
- Improved Learning of Multi-class Theories
- Empirical Evaluation
- Related Work
- Concluding Remarks
- References
- A Numerical Refinement Operator Based on Multi-Instance Learning
- Introduction and Background
- Method
- Experiments
- Datasets
- Experimental Setup
- Experimental Results
- Conclusion
- References
- Not Far Away from Home: A Relational Distance-Based Approach to Understanding Images of Houses
- Introduction
- Setting
- Approach
- Experimental Setup and Results
- Conclusions
- References
- Approximate Inference for Logic Programs with Annotated Disjunctions
- Introduction
- Logic Programs with Annotated Disjunctions
- Approximate Inference
- k-best Algorithm
- Monte Carlo Algorithm
- Experiments
- References
- Approximate Bayesian Computation for the Parameters of PRISM Programs
- Introduction
- Approximate Bayesian Computation
- PRISM
- ABC for PRISM
- Choice of Prior Distribution
- Choice of Distance Function
- Choice of Perturbation Kernel
- Experimental Results
- Conclusions and Future Work
- References
- Probabilistic Rule Learning
- Introduction
- Problem Specification
- Analysis
- ProbFOIL: A Probabilistic First Order Rule Learner
- Experiments
- Surfing
- Eulisis
- Conclusions
- References
- Interactive Discriminative Mining of Chemical Fragments
- Introduction
- Background
- The iLogCHEM System
- Integrating Structural Information in the Search
- Macros
- Molecular Properties
- Interactive Search and Refinement
- Conclusions
- References
- MMRF for Proteome Annotation Applied to Human Protein Disease Prediction
- Introduction
- Modular Multi-Relational Framework
- MMRF Applied to Human Protein Disease Prediction
- Results and Discussion
- Conclusions and Further Work
- References
- Extending ProbLog with Continuous Distributions
- Introduction
- ProbLog
- Hybrid ProbLog
- Distribution Over Continuous Subprograms
- Success Probabilities of Queries
- Exact Inference
- Experiments
- Conclusions and Future Work
- References
- Multivariate Prediction for Learning on the Semantic Web
- Introduction
- Related Work
- Statistical Modeling
- Defining the Sample
- The Random Variables in the Data Matrix
- Non-random Covariates in the Data Matrix
- Algorithms for Learning with Statistical Units Node Sets
- Experiments
- Data Set and Experimental Setup
- Results
- Conclusions and Outlook
- References
- Learning Action Descriptions of Opponent Behaviour in the Robocup 2D Simulation Environment
- Introduction
- Action Descriptions for Dynamic Domains
- Efficient Induction of Action Laws with IAction
- Modeling Opponent Behaviour
- Discretization of the Environment
- Representation
- Learning Results
- Previous Work and Conclusions
- References
- Hypothesizing about Causal Networks with Positive and Negative Effects by Meta-level Abduction
- Introduction
- Meta-level Abduction
- Reasoning about Positive and Negative Causal Effects
- Alternating Axioms for Causality
- Axiomatization with Default Assumptions
- Case Study: p53 Signal Networks
- Enumerating Tumor Suppressors
- Recovering Links in CDK Networks
- Discussion and Related Work
- Conclusion
- References
- BET : An Inductive Logic Programming Workbench
- Introduction
- Overview
- Design of BET
- Need for Standard Format
- BET Standard Format
- Positive Example File:
- Negative Example File:
- Background knowledge File:
- Language Restriction File:
- Class Diagram and Components of BET
- Support for Building ILP Systems in BET
- Integrating a Legacy System in BET
- Implementing a New ILP System in BET
- Related Work
- Aleph
- GILPS
- Advantages of BET over Existing Systems
- Summing Up BET
- References
- Seeing the World through Homomorphism: An Experimental Study on Reducibility of Examples
- Introduction
- Preliminaries
- Reducing the Examples
- Experiments
- Mutagenesis
- Predictive Toxicology Challenge
- CAD
- Aleph with Reduced and Non-reduced Data
- Conclusions
- References
- Learning Discriminant Rules as a Minimal Saturation Search
- Introduction
- Preliminaries, Notations and Saturation
- Saturation of an Example
- Minimal Saturation to Search
- Refinement Operator
- Refinements Selection
- Learning a Rule
- Advantages and Limitations
- Application to Constraint Satisfaction Problem and Experiments
- Conclusion
- References
- Variation of Background Knowledge in an Industrial Application of ILP
- Introduction
- Application Descriptions
- University Innovation Centre (UIC) Overview
- Tomato Application
- Predictive Toxicology
- Ondex: A Biological Background Knowledge Generator
- Experiments
- Materials and Methods
- Results and Discussion
- Conclusions and Further Work
- References
- Pruning Search Space for Weighted First Order Horn Clause Satisfiability
- Introduction
- Satisfiability in Horn Clauses
- T Operator
- Modified_T Step
- Modified_Weighted_SAT
- Results
- Conclusion and Future Work
- References
- Multi-relational Pattern Mining Based-on Combination of Properties with Preserving Their Structure in Examples
- Introduction
- Patterns and Mapix Algorithm
- Ideas and an Algorithm
- Experiments and Concluding Remarks
- References
- Learning from Noisy Data Using a Non-covering ILP Algorithm
- Introduction
- Related Work
- HYPER/N
- Handling of Noisy Data in HYPER/N
- Learning Multiple Clauses from Noisy Data
- Some Additional Improvements
- Experiments with Weather Data
- Conclusion
- References
- Can HOLL Outperform FOLL?
- Introduction and Motivations
- ?Progol: A Higher-Order ILP System
- Results and Applications
- Future Works
- References
- Incremental Learning of Relational Action Models in Noisy Environments
- Introduction
- Learning Problem
- Action Model
- Relational Representation
- Matching and Covering
- Incremental Relational Learning of an Action Model
- Sketch of the Algorithm
- Conservative Generalizations and Specializations
- Covering and Elimination of Irrelevant Rules
- Prediction with the Model
- Empirical Study
- Conclusion
- References
- When Does It Pay Off to Use Sophisticated Entailment Engines in ILP?
- Introduction and Motivation
- The -Subsumption Problem
- Subsumption versus Resolution
- Entailment Algorithms in ProGolem
- Time Complexity
- Empirical Evaluation
- Materials and Methods
- Results and Discussion
- Conclusions and Future Directions
- References
- Stochastic Refinement
- Introduction
- Preliminaries
- Stochastic Refinement Operators
- Stochastic Refinement Search
- Stochastic Refinement Search as a Gibbs Sampling Algorithm
- Stochastic Refinement Search as a Random Heuristic Search
- Examples of Stochastic Refinement Search
- Stochastic Refinement Relative to a Bottom Clause
- Divisibility of the Subsumption Lattice Relative to ?
- Conclusions
- References
- Fire! Firing Inductive Rules from Economic Geography for Fire Risk Detection
- Introduction
- Related Work
- The Fire DataSet
- Methodology
- The Background Knowledge
- Results and Discussion
- Conclusions
- References
- Automating the ILP Setup Task : Converting User Advice about Specific Examples into General Background Knowledge
- Introduction
- The Onion
- Converting Advice to Background Knowledge
- Experiments
- Test Beds
- Results and Discussion
- Related Work
- Conclusions and Future Work
- References
- Speeding Up Planning through Minimal Generalizations of Partially Ordered Plans
- Introduction
- Method
- Experiments
- Conclusions
- References
- Author Index
