The state of the art of the bioengineering aspects of the morphology of microorganisms and their relationship to process performance are described in this volume. Materials and methods of the digital image analysis and mathematical modeling of hyphal elongation, branching and pellet formation as well as their application to various fungi and actinomycetes during the production of antibiotics and enzymes are presented.
Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning, particularly in the evolution of inductive reasoning from pattern recognition, through initial approaches to symbolic machine learning, to recent techniques for learning relational concepts. In this book they provide an extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software engineering applications, including inductive program development, testing, and maintenance. Inductive Logic Programming includes a definition of the basic ILP problem and its variations (incremental, with queries, for multiple predicates and predicate invention capabilities), a description of bottom-up operators and techniques (such as least general generalization, inverse resolution, and inverse implication), an analysis of top-down methods (mainly MIS and FOIL-like systems), and a survey of methods and languages for specifying inductive bias. Logic Programming series
This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.
Wewishtothank AlfredHofmannandAnnaKramerofSpringer-Verlagfortheircooperationin publishing these proceedings. Finally, we gratefully acknowledge the nancial supportprovidedbythesponsorsofILP-99.
Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.
Inductive logic programming is a new research area emerging at present. Whilst inheriting various positive characteristics of the parent subjects of logic programming an machine learning, it is hoped that the new area will overcome many of the limitations of its forbears. This book describes the theory, implementations and applications of Inductive Logic Programming.
An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material. A unique advantage to this work is the combination of AI, Prolog and Logic. Each technique is accompanied by a program implementing it. Seeks to simplify the basic concepts of logic programming. Contains exercises and authentic examples to help facilitate the understanding of difficult concepts.
