Knowledge-driven computing: knowledge engineering and intelligent computations

Knowledge-Driven Computing constitutes an emerging area of intensive researchlocated at the intersection of Computational Intelligence and Knowledge Engineering with strong mathematical foundations. It embraces methods and approaches coming from diverse computational paradigms, such as evolutionary computation and nature-inspired algorithms, logic programming and constraint programming, rule-based systems, fuzzy sets and many others. The use of various knowledgerepresentation formalisms and knowledge processing and computing paradigms isoriented towards the efficient resolution of computationally complex and difficult problems. The main aim of this volume has been to gather together a selection of recent papers providing new ideas and solutions for a wide spectrum of Knowledge-Driven Computing approaches. More precisely, the ultimate goal hasbeen to collect new knowledge representation, processing and computing paradigms which could be useful to practitioners involved in the area of discussion.Presents latest results in Knowledge-Driven Computing INDICE: From the contents Temporal Specifications with FuXTUS. A Hierarchical Fuzzy.- Approach Bond Rating with pGrammatical Evolution.- Handling the Dynamics of Norms.- Experiments with Grammatical Evolution in Java.- Processing and Querying Description Logic Ontologies Using.- Cartographic Approach.- Rough Sets Theory for Multi-Objective Optimization Problems.- On Use of Unstable Behavior of a Dynamical System Generated by Phenotypic Evolution.- Temporal Specifications with XTUS. A Hierarchical Algebraic Approach.- A Parallel Deduction for Description Logics with ALC Language.- Applications of Genetic Algorithms in Realistic Wind Field.- Simulations.- Methodologies and Technologies for Rule-Based Systems.- Design and Implementation Towards Hybrid Knowledge Engineering.- XML Schema Mappings Using Schema Constraints and Skolem Functions.- Outline of Modification Systems.- Software Metrics Mining to Predict the Performance of Estimation of Distribution Algorithms in Test Data Generation.

• ISBN: 978-3-540-77474-7
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 325
• Fecha Publicación: 01/02/2008
• Nº Volúmenes: 1
• Idioma: Inglés