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Theory and Practice of Logic Programming emphasises both the theory and practice of logic programming. Logic programming applies to all areas of artificial intelligence and computer science and is fundamental to all of them. Among the topics covered are AI applications that use logic programming, logic programming methodologies, specification, analysis and verification of systems, inductive logic programming, multi-relational data mining, natural language processing, knowledge representation, nonmonotonic reasoning, semantic web reasoning, databases, implementations and architectures and constraint logic programming.
: Journal
The Review of Symbolic Logic is designed to cultivate research on theborders of logic, philosophy, and the sciences, and to supportsubstantive interactions between these disciplines. The journalwelcomes submissions in any of the following areas, broadly construed: - The general study of logical systems and their semantics,including non-classical logics and algebraic logic; - Philosophical logic and formal epistemology, including interactions with decision theory and game theory; - The history, philosophy, and methodology of logic and mathematics, including the history of philosophy of logic and mathematics; - Applications of logic to the sciences, such as computer science, cognitive science, and linguistics; and logical results addressing foundational issues in the sciences. The Review welcomes submissions in all these areas. (For informationon how to submit papers, see Guidelines below.) The Review beganpublication in June 2008.
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Acta Numerica is the top-cited journal for the last two years in MathSciNet. Its annual collection of review articles includes survey papers by leading researchers in numerical analysis and scientific computing. The papers present overviews of recent advances and provide state-of-the-art techniques and analysis. Covering the breadth of numerical analysis, articles are written in a style accessible to researchers at all levels and can serve as advanced teaching aids. Broad subject areas for inclusion are computational methods in linear algebra, optimization, ordinary and partial differential equations, approximation theory, stochastic analysis and nonlinear dynamical systems, as well as the application of computational techniques in science and engineering and the mathematical theory underlying numerical methods.
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