Inductive Logic Programming - Info and Reading Options

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Intro -- Title Page -- Preface -- Organization -- Table of Contents -- Actions, Causation and Logic Programming -- Challenges to Machine Learning: Relations Between Reality and Appearance -- Introduction -- Elements, Atoms, and Molecules -- Elements, Atoms, Molecules - Formulas -- Appearance and Reality -- Another Start on Three Dimensional Objects -- A Puzzle About Inferring Reality from Appearance -- Formulas for Appearance and Actions in the Puzzle -- How Should a Computer Discover the Reality? -- Limitations of Our Treatment and Remarks -- References -- First-Order Probabilistic Languages: Into the Unknown -- Introduction -- ATaxonomyofFOPLs -- Outcome Spaces -- Specificity -- Conditional Probabilities Versus Weights -- Independent Choices Versus Probabilistic Dependencies -- Known Versus Unknown Objects -- Bayesian Logic (BLOG) -- Syntax -- Semantics -- Design Features -- LearninginFOPLs -- Parameters -- Dependency Structure -- Functions and Types -- Conclusion -- References -- Integration of Learning and ReasoningTechniques -- On the Connection Between the Phase Transition of the Covering Test and the Learning Success Rate -- Introduction -- Experiments -- Conclusion -- References -- Revising Probabilistic Prolog Programs -- ProbLog: Probabilistic Prolog -- Revising ProbLog Theories -- The ProbLog Theory Revision Algorithm -- Conclusion -- Reference -- Inductive Logic Programming for Gene Regulation Prediction -- Introduction and Background -- Data and Representation -- Summary of Experimental Results and Conclusion -- References -- QG/GA: A Stochastic Search for Progol -- Introduction -- QG Algorithm -- QG and QG/GA in Progol -- Conclusions -- References -- Generalized Ordering-Search for Learning Directed Probabilistic Logical Models -- Introduction -- Generalized Ordering-Search -- Experiments -- References

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