Inductive Logic Programming

Inductive Logic Programming

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Inductive logic programming is a new research area formed at the intersection of machine learning and logic programming. While the influence of logic programming has encouraged the development of strong theoretical foundations, this new area is inheriting its experimental orientation from machine learning. Inductive Logic Programming will be an invaluable text for all students of computer science, machine learning and logic programming at an advanced level.
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Product details

  • Hardback | 544 pages
  • 159 x 236.7 x 34.5mm | 1,079.89g
  • Academic Press Inc
  • San Diego, United States
  • English
  • 0125097158
  • 9780125097154

Table of contents

Inductive logic programming, S. Muggleton; extensions of inversion of resolution applied to theory completion, Celine Rouveirol; generalization and learnability - a study of constrained atoms, C.D. Page Jr and A.M. Frisch; learning theoretical terms, R.B. Banerji; logic programme synthesis from good examples, C.X. Ling; a critical comparison of various methods based on inverse resolution, C.X. Ling and M.A. Narayan; non-monotonic learning, M. Bain and S. Muggleton; an overview of the interactive concept-learner and theory revisor, Clint; a framework for inductive logic programming, P.A. Flach; the rule-based systems project - using confirmation theory and non-monotonic logics for incremental learning, D. Gabbay, et al; relating relational learning algorithms, D.W. Aha; machine intervention of first-order predicates by inverting resolution, S. Muggleton and W. Buntine; efficient induction of logic programmes, S. Muggleton and C. Feng; constraints for predicate invention, R. Wirth and P. O'Rorke; refinement graphs for FOIL and LINUS, S. Czeroski and N. Lavrac; controlling the complexity of learning in logic through syntactic and task-oriented models, J.U. Kietz and S. Wrobel; efficient learning of logic programme with non-determinate, non-discriminating literals, B. Kijsirikul, et al; an information-based approach to integrating empirical and explanation-based learning, M.J. Pazzani, et al; analogical reasoning for logic programming, B. Tausend and S. Bell; some thoughts on inverse resolution, G. Sablon, et al; experiments in non-monotonic first-order induction, M.Bain; learning qualitative models of dynamic systems, I. Bratko, et al; the application of inductive logic programming to finite element mesh design, B. Dolsak and S. Muggleton; inducing temporal fault diagnostic rules from a qualitative model, C. Feng; in ductive learning of relations from noisy examples, N. Lavrac and S. Dzeroski; learning chess patterns, E. Morales; applying inductive logic programming in reactive environments, D. Hume and C. Sammut.
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About S. Muggleton

Edited by Stephen Muggleton
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