Knowledge-Driven Computing [electronic resource] : Knowledge Engineering and Intelligent Computations / edited by Carlos Cotta, Simeon Reich, Robert Schaefer, Antoni Ligęza.

Temporal Specifications with FuXTUS. A Hierarchical Fuzzy Approach -- Bond Rating with ? Grammatical Evolution -- Handling the Dynamics of Norms – A Knowledge-Based Approach -- Experiments with Grammatical Evolution in Java -- Processing and Querying Description Logic Ontologies Using Cartographic Approach -- Rough Sets Theory for Multi-Objective Optimization Problems -- How to Acquire and Structuralize Knowledge for Medical Rule-Based Systems? -- 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 -- Design and Analysis of Rule-based Systems with Adder Designer -- A Query-Driven Exploration of Discovered Association Rules -- A Universal Tool for Multirobot System Simulation -- Advancing Dense Stereo Correspondence with the Infection Algorithm.

Knowledge-Driven Computing constitutes an emerging area of intensive research located 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 knowledge representation formalisms and knowledge processing and computing paradigms is oriented 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 has been to collect new knowledge representation, processing and computing paradigms which could be useful to practitioners involved in the area of discussion. To this end, contributions covering both theoretical aspects and practical solutions, and dealing with topics of interest for a wide audience, and/or cross-disciplinary research were preferred.